Communication system and method for narrowcasting

ABSTRACT

A communication system with client devices in communication with at least one communication network. User data stores are also in communication with the communications network and store user data of users using respective ones of the client devices. Offer data stores also in communication with the communications network store offers from merchants. A narrowcasting engine includes an active data gathering module to collect the user data, and an active learning module to generate a user profile based on the user data. The communication engine selects dynamically offers from the offer data store based on the profile, and communicates the selected offers in the offer data store to the users.

This application claims the benefit of U.S. Provisional PatentApplication Nos. 60/831,193 filed on Jul. 17, 2006, 60/850,263 filed onOct. 10, 2006, 60/924,591 filed on May 22, 2007, and 60/924,592 filed onMay 22, 2007, which are incorporated herein by reference.

BACKGROUND

1. Field

The present invention relates to a communication system and method, andmore particularly, to a communication system and method fornarrowcasting information based on active data gathering and activelearning, providing merchant network services, and transactionprocessing for accumulating and redeeming rewards on a registered cardwith a spend, save, and give feature.

2. Discussion of the Related Art

Traditional communication systems and methods of reaching desiredaudiences among the masses mainly rely upon the broadcasting model, suchas mass mailings and television/radio advertisements to inform potentialconsumers of various offerings in the hopes of increasing business.

But, the traditional broadcasting model is highly inefficient andresearch has shown that only a small fraction of the population pays anyattention to these broadcasts. Using free standing inserts (“FSI”) as anexample, it has been estimated that merchants spend about $3 billionannually on marketing campaigns, while the amount actually redeemed bythe consumers (i.e., those that have responded) is estimated to be onlyabout $30 million, or only about a 1% response rate. Response rates toother forms of broadcast are generally unknown and difficult toquantify.

Merchants also use emails as a cheaper marketing channel in an effort tomake the marketing process more efficient. But, consumers becomeoverwhelmed with unsolicited advertisement emails (i.e., “spam”) and allof its various forms of unsolicited advertisements, such as “pop-ups”(e.g., unsolicited advertisements that pop up during Internet use) anddiscard these indiscriminant communications. The result is tremendouswaste in marketing spend for a miniscule return.

In an attempt to focus the communications to be more relevant, thecommunications industry has recently been developing ways to matchinformation to recipients to find more information of interest. This isoften referred to as targeted marketing. This can be based ondemographic data. But, this information, for example gender and locationof residence, is generally insufficient to assure offers that may beinteresting to the user. To supplement this information, others try toobtain information to profile the users' interests.

Unfortunately, profiles are only as useful as the information providedby the user. If the user provides false information or does not provideany information during registration, the targeted information will beirrelevant and therefore useless. Usually profiling is achieved bypresenting users with vague questions to elicit the requiredinformation. For example, a typical “general interest” category may belisted as “outdoors.” If a user designates “outdoors” as an interestduring registration, the user may get advertisements and/or offersranging from hiking shoes to picnic accessories to travel magazinesbecause such a preference is so vague. These so-called targetedcommunications are only slightly more effective than generalbroadcasting.

Some service providers have begun to supplement the vague userpreference categories with tracked user activities, such as purchasesmade by the user. But, targeted advertisements and offers from theseknown systems are ineffective because the offered contents are almostalways done in hindsight, i.e., based on past activities and, therefore,tend to be too late.

In addition to targeting communications, certain business use incentivesto attract customers. For example, coupons are one of the vehicles usedto encourage consumers to purchase specific products and/or spend at aparticular business. Currently, some 300 billion coupons are distributedannually in the United States through an approximately $6 billionnational coupon industry. Approximately half of the estimated $6 billiongoes towards the actual incentive with the other half going towardsadministration. Combining this estimate with the fact that approximately99% of the coupons end up in the trash, unused, and unredeemed,consumers only benefit from approximately $30 million in redeemedincentives (i.e., only about 0.5% of the $6 billion actually goes to theconsumers).

What is needed, therefore, is a cost-efficient and convenient incentiveredemption system and method that would provide benefits to both theconsumers and merchants.

Another type of incentive typically used to draw consumers to usage is arewards/loyalty program. The main marketing thrust of a rewards/loyaltyprogram is to register, maintain, and increase consumer usage of aparticular merchant or service provider by offering various incentivesto the members of the program. Approximately 160 million people belongto an airline loyalty program, and approximately 32 million peoplebelong to a credit card rewards program. Businesses spend an estimated$25 billion on rewards and incentives. Companies spend an estimated $50million on employee rewards and recognition programs.

One of the most popular incentives used by rewards/loyalty programs isthe “points” system. The idea is the member accumulates certain numberof points for specified activities defined by the sponsor of the program(e.g., 1 point for every $1 spent, 1 point for visiting a sponsoringmerchant, etc., 1 point for every mile traveled, etc.). Then, the memberis given the opportunity to redeem the accumulated points for a“reward.” The reward may be a product, service, or even cash that can beobtained by redeeming a specified number of points (e.g., 1¢ back forevery point, a free camera for 3,400 points, a free plane ticket for50,000 points, etc.).

There are various disadvantages of the current points based incentiveprograms. First, the rewards available for redemption are extremelylimited. Typically, products available for redemption are generallyproducts that are overstocked or outdated and are sitting in warehouses,either purchased by the sponsors at a discount or contracted by thewarehouse vendors to help move the products. Therefore, majority of themembers find themselves ordering products they do not need or do notfind very appealing to burn the points before losing them or lettingthem go to waste.

Second, the redeemable price and the cost spent are generallydisproportionate. That is to say, the amount the member must spend toaccumulate a point is far greater than the point is worth when the timecomes to redeem it. For example, many reward programs equate 1 point forevery $1 spent. But, a typical rewards catalog will list a camera, forexample, with a street price of $150 to be redeemable with 3,400 points.As another example, typical airlines equate 1 point for every 1 miletraveled. But, to obtain a free plane ticket to a destination within thecontinental United States, typical airlines require 50,000 points ormore. Given that the distance between the east coast and the west coastis only about 3,000 miles; such an “incentive” does not necessarilyencourage a consumer to become a member just for the reward. Even cashback reward programs typically only give back 1% of the amount spent,and many sponsors push to apply the cash back as credit against the billor issue the cash as gift certificates.

Third, the redeeming process is extremely inefficient and inconvenient.The typical wait time between requesting for redemption of the points toactually receiving the reward is about 4 to 8 weeks, depending on therequested reward. Even when cash back is requested, the processing timegenerally takes about 4 to 6 weeks, especially when a check or giftcertificate is to be issued. Because of the delay in the processing, thepoints total will not reflect the pending redemption amount until thepoints are redeemed. Accordingly, the offered “rewards” do not appeal toconsumers who understand that the economics behind the rewards programare not only inconvenient but are not really incentives at all.

SUMMARY OF THE INVENTION

Accordingly, one aspect of this invention provides for a communicationsystem with client devices in communication with at least onecommunication network. user data stores are also in communication withthe communications network and store user data of users using respectiveones of the client devices. offer data stores also in communication withthe communications network store offers from merchants. A narrowcastingengine includes an active data gathering module to collect the userdata, and an active learning module to generate a user profile based onthe user data. The communication engine selects dynamically offers fromthe offer data store based on the profile, and communicates the selectedoffers in the offer data store to the users.

The user data collected by the active data gathering module can includedemographic, behavioral, and preference data. The preference data caninclude a request for future reminders of past lost opportunities; andthe behavioral data can include at least one of click-throughs, hovers,and search terms of offers presented on the client device.

In addition, the active data gathering module allows a user toparticipate in a preference game for obtaining preference data of theuser.

Preference building can be accomplished by presenting questions andoffers available to a user on a user interface, in which the questionsand offers are dynamically created for the user based on initial userdata. Answers to the questions are received through the user interfaceand processed to generate preference data of the user. Offerspresentation can then be changed dynamically on the user interface innear real-time based on the preference data.

In some instances the initial user data is supplied by a network towhich the user belongs. Such network could be a sponsor of aloyalty/rewards program.

Preference building can also be accomplished by presenting to a user acalendar interface that includes indicia for past offers from merchants.A selection interface then allows the user to designate past offers thatthe user wishes to be reminded of in future offerings.

Further, the profile generated by the active learning module includes apersona type, selected from a predetermined set of personas, for users.The profile can also includes an indication of a life stage of users.

The system can also extend to an offer datastore for storing offers frommerchants and an offer ranking module for rank the offers in the offerdatastore based on popularity. Merchants can also use an offer biddingmodule to modify the offers based on the rank of the offer from theoffer ranking module.

The invention also encompasses a method for communication, includingcollecting user data of users in a user datastore; storing merchantoffers in an offer datastore; generating a persona of users based on theuser data; storing the persona in a persona datastore; segmenting theoffers in the offer datastore based on the persona stored in the personadatastore; segmenting users into segmentation cells; matching the offermixes with the user segmentation cells based on rules associated witheach cell; and transmitting the offer mix to the users.

The rules can include at least one of suppression rules, designationrules, and offer mix integration rules and the offer mix integrationrules determine which offers are to be combined to form the offer mix.the persona can be defined as before.

In addition, the invention extends to a system for creating a merchantoffer that uses an enrollment module to solicit and receive merchantinformation and offer information; a heat map module to display on theclient devices consumer activity on the communication network; and adatastore to store the merchant information and the offer information.

The display of the consumer activity generated by the heat map moduleincludes a graphical representation depicting varying levels of activityover a period of time based on at least one of product, product type,and merchant. The graphical representation includes at least one ofvarying shapes, sizes, or color in proportion to the varying levels ofactivity.

The system also extends to a card transaction processing module thatgenerates purchase transaction data associated with a payment card. Thisoperates with an offer datastore including offers from merchants; and atransaction matching module that receives the purchase transaction dataassociated with the payment card and match the purchase transaction withthe merchants in the offer datastore. A rewards module determines anincentive to be applied to the payment card based on any offerassociated with the matched merchant and generates a qualifiedtransaction data to be transmitted to an issuer of the payment card.

Also, the system can include an offer datastore including offers frommerchants; and a registered card module to register payment cards to beused for a purchase transaction. These work together with a transactionmatching module that matches the purchase transaction with the merchantsin the offer datastore; and a rewards module that determines anincentive to be applied to the payment cards based on any offerassociated with the matched merchant and generates a qualifiedtransaction data to be transmitted to an issuer of the payment cards.

A card processing module can be added to determine the amount to becredited back to the payment cards identified in the qualifiedtransaction data. Another addition is a statement generator thatgenerates a card statement including itemized listing of the purchasetransaction and the amount credited back to the payment cards. A pointsmodule can be used to convert a designated number of points into amonetary value and apply the converted monetary value to the purchasetransaction. The points module could also convert a designated number ofpoints into a monetary value and apply the converted monetary value to asaving account. Alternatively, the points module could convert adesignated number of points into a monetary value and apply theconverted monetary value to a charity account.

The invention also extends to testing a market segmentation bysegmenting users into user segmentation cells, in which the usersegmentation cells being associated with market segments. Messagesincluding an offer mix are generated for the user segmentation cellsassociated with the users. Messages are then sent to a subset of theusers associated with the user segmentation cells. These users'responses are then analyzed to identify a type of message eliciting ahigh response rate. The messages can then be refined based on theidentified type of message; and the refined messages can then be sent toall of the users of the segmentation cells.

It is possible to generate a first message for a first subset of theusers, and a different second message for a second subset of the users.Typically, the generating, sending and analyzing processes are repeatedfor a predetermine number of times.

Thus, the systems, sub-systems and methods of this invention havenumerous facets, many of which can be combined in differentconfigurations.

Additional features and advantages of the invention will be set forth inthe description which follows, and in part will be apparent from thedescription, or may be learned by practice of the invention. Theobjectives and other advantages of the invention will be realized andattained by the structure particularly pointed out in the writtendescription and claims hereof as well as the appended drawings.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and areintended to provide further explanation of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the invention and are incorporated in and constitute apart of this specification, illustrate embodiments of the invention andtogether with the description serve to explain the principles of theinvention. In the drawings:

FIG. 1A is an overview of the various components of the presentinvention;

FIG. 1B is a block diagram view of the loyalty/rewards system;

FIG. 2 is a system diagram illustrating an exemplary embodiment of thepresent invention;

FIG. 3 is a block diagram illustrating member segmentation;

FIG. 4 is a block diagram illustrating segmentation cells;

FIGS. 5A-5B are block diagrams illustrating offer segmentation and anexemplary offer mix;

FIGS. 6A-6B are block diagrams illustrating a branding approach and anexemplary branding result;

FIGS. 7-11 are views illustrating the testing and launch processaccording to the present invention;

FIG. 12 is a block diagram that illustrates active data gatheringaccording to an exemplary embodiment of the present invention;

FIG. 13 is a block diagram that illustrates active learning according toan exemplary embodiment of the present invention;

FIG. 14 is a diagram that illustrate the registration and activation ofbenefit sites in accordance with the present invention;

FIG. 15 is a diagram that illustrates the preference questions generatedbased on segmented cells;

FIG. 16 is an exemplary web page illustrating intelligent questioningaccording to an exemplary embodiment of the present invention;

FIGS. 17A-17G are exemplary illustrations of the data collection processaccording to the present invention;

FIG. 18 is a chart illustrating an exemplary behavioral analysisaccording to the present invention;

FIG. 19 is an exemplary profile of a persona in accordance with thepresent invention;

FIGS. 20-22 are exemplary illustrations of the various reports generatedin accordance with the present invention;

FIG. 23 is a diagram illustrating a purchase funnel of the presentinvention;

FIG. 24 is a block diagram illustrating an exemplary data flow accordingto the present invention;

FIGS. 25-30 show an exemplary embodiment of segmentation and preferencegathering/learning according to the present invention;

FIG. 31 is a view of an exemplary embodiment of the auto-enroll moduleof the present invention;

FIG. 32 is a flowchart describing an exemplary enrollment processaccording to the present invention;

FIG. 33 is a flowchart describing an exemplary offer management processaccording to the present invention;

FIG. 34 is an exemplary view of a heat map according to an exemplaryembodiment of the present invention;

FIG. 35 is an exemplary view of various examples of heat maps;

FIG. 36 is an exemplary view of an offer rank module of the presentinvention;

FIG. 37 is an exemplary view of an offer bid module of the presentinvention;

FIG. 38 is an exemplary view of merchant mapping;

FIG. 39 is a merchant workflow diagram of an exemplary embodiment of thepresent invention;

FIGS. 40A-40D are exemplary screenshots shown during an auto-enrollprocess in accordance with the present invention;

FIG. 41 is an exemplary view of an enrollment notification in accordancewith the present invention;

FIG. 42 is an exemplary view of a login page in accordance with thepresent invention;

FIGS. 43A-43D are exemplary screenshots shown during a merchant setupprocess in accordance with the present invention;

FIGS. 44A-44E are exemplary screenshots of various merchant tools inaccordance with the present invention;

FIG. 45 is a block diagram illustrating an exemplary embodiment of apayment processing system in accordance with the present invention;

FIG. 46 is a block diagram illustrating an exemplary embodiment of aregistered card processing system in accordance with the presentinvention;

FIG. 47 is a workflow diagram illustrating an exemplary registered cardprocess in accordance with the present invention;

FIG. 48 is a view of an exemplary registered card statement inaccordance with the present invention;

FIG. 49 is a view of exemplary rules available through the registeredcard system;

FIGS. 50-51 are illustrations of exemplary embodiments for processingcard transactions;

FIG. 52 is a flow diagram illustrating an exemplary process for earningpoints in accordance with the present invention;

FIGS. 53-54 are flow diagrams illustrating exemplary processes forburning points in accordance with the present invention;

FIG. 55 is a diagram illustrating an exemplary process for a registeredcard purchase transaction; and

FIGS. 56A-56F are exemplary views of a portal for accessing arewards/loyalty program.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE INVENTION

Reference will now be made in detail to the preferred embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings.

Overview of the Narrowcasting System and Method

The present invention is directed to presenting relevant communicationsto relevant audiences at the relevant time. In particular, the systemand method for narrowcasting is directed towards presentingcommunications in a discrete manner via closed-loop marketing. As usedherein, “closed-loop” marketing refers to a marketing channel where amarketing campaign can be traced from its launch to the end user. Thenarrowcasting system and method of the present invention is directed tocommunicating relevant offers from providers of goods and services torelevant potential consumers at relevant times, although the system andmethod of the present invention may be applied to other venues andapplications without departing from the scope of the invention.

Relevance of the communicated offers is only as accurate as thepreference data provided by the users. While users provide some type ofpreference data to merchants, accurate preference data is difficult toobtain without an established trust. As mentioned above, prior artsolutions of obtaining customer preference data have relied on formalsurveys or questionnaires. Preference data elicited from these prior artmechanisms are generally of lower relevance and marketing value. Bycontrast, preference data provided by customers in affirmativelyrequesting for a specific merchant offer during the course of onlineshopping as a matter of customer service, for example, is highlyrelevant.

Accordingly, the present invention is directed to establishing a systemand method that provides “customer service” experience to the usersrather than the prior art approach of “selling” products and services tothe users. To illustrate, in accordance with the present invention, aconsumer that is shopping for a computer laptop may view an expiredoffer for a computer laptop from a specific merchant (e.g., Dell). Thepresent invention allows for the consumer to request a reminderregarding similar offers in the future. At this point, the consumer hasmade an explicit request about a specific merchant offer regarding aspecific product. Hence, similar offers in the future will likely resultin a higher rate of purchase (i.e., a conversion rate). The higher therate of conversion, the more profitable the customer interactionbecomes, and the more valuable the marketing service is to the merchant.

As users realize that they are being “serviced” by relevant offersand/or choices at the most opportune times, trust is increased in thesystem's ability. As trust increases, usage of the system increases. Inturn, as usage increases, more accurate preference data are obtained,which then act to provide even more relevant offers at more relevanttimes.

The present invention increases the “trust” aspect of the users'experience by leveraging the relationship already established betweenthe users and their affiliated “networks” (e.g., employers, financialinstitutions, institutions, etc.) by implementing the system and methodof the present invention on a “rewards/loyalty” platform. However, thesystem and method of the present invention may be implemented on othertypes of business models without departing from the scope of theinvention. In the exemplary embodiment of the present invention, thesystem and method of the present invention operates on a rewards/loyaltyplatform of networks of which the users are already members, instanttrust is already created between the system of the present invention andthat of the users. Initial sets of data for the potential users (e.g.,demographic, behavioral, preference information) are provided by thenetworks already in a trust relationship with the users, thereby makingthe initial preference analysis already highly relevant and accurateeven before usage by the users. Therefore, the first impression of thesystem of the present invention to first time users is one of relevanceand trust, thereby setting the tone for high usage.

While “relevance” of offers can increase usage by increasing trust,breadth (i.e., quantity) and depth (i.e., quality) of products andservices available on the system are integral to increasing usage of thesystem. Moreover, as explained in the Background of the Invention,quality of the incentives as well as convenience of redeeming theincentives are also significant factors in increasing the usage of therewards/loyalty program. In accordance with the present invention, thenarrowcasting system of the present invention includes various paymentand redemption modules to increase the usage of the rewards/loyaltyprogram by benefiting the members, participating merchants, and theprogram sponsors alike.

FIG. 1A shows an overview of the various components of the presentinvention. As shown, the system of the present invention generallyincludes a user network 10, merchant network 20, and a loyalty/rewardssystem 30. As explained in detail below, the loyalty/rewards system 30integrates with other existing, components such as the credit, debit,point-of-sales (POS) systems 40, cellular phone systems 50, charityorganizations 60, and financial/asset management systems 70.

FIG. 1B shows a block diagram view of the loyalty/rewards system 30 ofFIG. 1A. As shown in FIG. 1B, the loyalty/rewards system 30 includescustomer network technology component 32, merchant network technologycomponent 34, payment services technology 36, and narrowcastingtechnology 38. Each of these components is described in more detailbelow.

Narrowcasting

“Narrowcasting,” as used in the exemplary embodiment of the presentinvention, is a scientific approach to marketing using computer,behavioral, and statistical science to target a market. Narrowcasting isa communications model that provides the right product to the rightcustomer at the right time through the right communications channel. Thenarrowcasting system and method of the present invention accomplishesthis task by using information obtained from trusted relationships,explicit preferences designated by the users, and inferred behavioralpreferences obtained by tracking users' activities to dynamicallyprovide relevant offers at relevant times to the right users. Unlikeprior art systems, the narrowcasting system of the present inventioncreates synthetic behavioral profiles referred to herein as “personas”with associated rules to match offers to the users. The matched offersare then sent via the most effective communications channel. Anexemplary embodiment of the narrowcasting system and method of thepresent invention is described below.

Narrowcasting System

As shown in FIG. 1B, the narrowcasting system 38 includes an active datagathering component 38 a and active learning component 38 b. The detailsof these components are described with reference to FIG. 2.

FIG. 2 shows an overall diagram of an exemplary embodiment of thepresent invention. In its simplest form, narrowcasting engine 250dynamically matches the most relevant offers from various providers ofgoods and services, referred to herein as “merchants,” (260 a, 260 b) tothe most relevant users enrolled with the narrowcasting system (290 a,290 b) at the most relevant time. To accomplish this end, thenarrowcasting system of the present invention includes variouscomponents.

As shown in FIG. 2, the narrowcasting system of the present inventionincludes a user network member database 210 and a merchant offerdatabase 260. The term “user network” as used herein refers to a groupto which the users belong. Examples of user networks include employers(i.e., HR), institutions (e.g., universities, credit card companies),affinity groups (e.g., trade groups), and other entities with memberswho are networked through the entity. Although not shown, networksregister with the narrowcasting system of the present invention to setupnarrowcasting services for their members. For example, the narrowcastingsystem 38 may be used by the networks to provide benefit services (e.g.,perks, loyalty, or reward programs) to their members. The narrowcastingsystem 38 may provide the offers in the offer database 260 to theregistered networks' members as network membership benefits.Accordingly, the network member database 210 contains information aboutthe members provided by the registering networks including demographicand other personal information. Therefore, most of the users of thenarrowcasting system 38 are members of a registered network. In anotherexemplary embodiment, access to the narrowcasting system 38 may also begranted to non-network members, such as network members' family membersand associated individuals.

Merchant Offer Database

Merchant offer database 260 contains various offers, such as incentivesand discounts, offered by various merchants (260 a, 260 b). In theexemplary embodiment of FIG. 2, merchants may be divided into twocategories: (1) hosted merchants 260 a, and (2) network affiliatedmerchants 260 b. Hosted merchants 260 a are a class of merchants whoregister with the narrowcasting system 38 to provide offers to theusers. Network affiliated merchants 260 b area class of merchants whohave a working relationship with the members' networks. For example, aparticular merchant (e.g., a flower shop) may be a sponsored merchantfor a particular network (e.g., an affiliated credit card network) whileanother merchant (e.g., bookstore) has no affiliation with any of themember networks. Therefore, the sponsored merchant is classified as anetwork affiliated merchant 260 b while the non-affiliated merchant isclassified as a host merchant 260 a.

Narrowcasting Engine

The narrowcasting engine 250 is a computer programmed to dynamicallymatch the users (290 a, 290 b) with the offers in the merchant offerdatabase 260 while updating and facilitating various transactions andactivities provided by the narrowcasting system 38. In particular, thenarrowcasting engine 250 is connected to a user preference data store240 a, a user behavioral data store 240 b, rules/personas data store 240c, and a demographic data store 240 d. The data stores 240 a, 240 b, 240c, and 240 d may be any type of appropriate data storage device known inthe art. The data stores 240 a-240 d may each be independent storagedevices, sub-portions of a single storage device, or any combinationthereof without departing from the scope of the invention. Moreover, theuser preference data, behavioral data, rules/personas, and demographicdata may be stored as flat files or as records in a relational databaseor databases without departing from the scope of the invention.

Demographic data includes personal information about the user, such asname, company, title, location, gender, age, marital status, etc.obtained from the network member database 210 and directly communicatedby the users during an enrollment stage. Behavior data include suchinformation as general interests, viewing and transaction activities onthe system (e.g., click-throughs), and inferred specific items ofinterest of the user. The user preference data, as described in moredetail below, are directed to specific requests from the users thatindicate the users' future preferences. The narrowcasting engine 250also dynamically updates the user preference data from users'activities, constantly supplementing the users' preference data withnewly obtained information. The types of data and how they are obtainedare explained in further detail below.

Based on the users' demographic, behavioral, and preference information,the narrowcasting engine 250 creates synthetic behavioral profiles(i.e., “personas”) and stores/updates the profiles in the rules/personasdata store 240 c. Using the personas and rules associated thereto, thenarrowcasting engine 250 selects the most appropriate offers from theoffer database 260 for each of the users (290 a, 290 b) and presents thedynamically generated communications to the users (290 a, 290 b) throughthe network program portal (280 a, 280 b). Personas are explained inmore detail below.

The narrowcasting engine 250 tracks users' activities on the system anddynamically updates user preference, behavioral, and demographic datainto the appropriate data stores 240 a, 240 b, and 240 d, respectively,based on the users' activities. Some of the activities facilitated andtracked by the narrowcasting engine 250 include user selections andviewing activities on the network program portal (280 a, 280 b). If theuser takes advantage of the offers presented in the narrowcastedcommunication, the narrowcasting engine 250 tracks the users'transaction regarding the accepted offer through the transaction module270.

Transaction Module

The transaction module 270 tracks the transaction between the merchantrelated to the accepted offer and the user to report the users'activities. The transaction module 270 also calculates the payments dueto or from the networks, merchants, and to the narrowcasting system. Thetransaction module 270 is described in more detail below with regard tothe registered card implementation.

Member Segmentation

The member segmentation module 220 processes information regarding themembers to initially set up membership registration including assignmentof member identification (“member ID”), membership validations, offersuppression/designation, etc. Upon log in, the registered members arethen directed to the program enrollment module 230. The programenrollment module 230 creates and/or updates profile information of theuser, such as designation of preferences as well as information missingfrom the network member database 210. The member segmentation module 220and program enrollment module 230 are explained in further detail below.

As explained above, an exemplary embodiment of the present invention isa system and method of narrowcasting various offers and incentives tomembers of affinity groups/networks. For instance, one example is anemployer sponsored portal accessible by an employee to access companybenefits hosted by the narrowcasting system 38. Such employer-basedportals may be set up, in part, to allow employees to obtain perks andbenefits, such as negotiated discounts from affiliated merchants 260 b,for example. Another example is a financial network, such as credit cardcompanies, who set up portals accessible by card members to obtainloyalty rewards and perks.

To host such benefits programs, a network registers with thenarrowcasting system 38 and provides information about their members. Asexplained above, member information is stored in the network memberdatabase 210. The member segmentation module 220 sets up member useraccess accounts by, for example, validating the member users, assigningmember user IDs, and offer suppression/designation/integration rules.The offer suppression/designation/integration rules are a set of rulesset by the user networks to suppress offers from specified merchantsand/or service providers, to designate specified merchants and/orservice providers to provide offers, and integrate various offerstogether for narrowcasting. An example of offer suppression is asponsoring network (e.g., a product company) may not want their membersto receive offers from their competitors. An example of offerdesignation is a sponsoring network (e.g., a credit card company) maywant their members to specifically take advantage of offers fromaffiliated merchants.

More specifically, as shown in FIG. 3, the member segmentation module220 obtains members' information from the network member database 210.Member data generally includes demographic data and preference dataprovided to the user network. For instance, if the user network is anemployer-sponsored website for its employees, the employer already hassubstantial information about the employee, such as name, address, andposition data. If the user network is a credit card company, the creditcard company has the card member's information, such as name, address,income, and credit history including debt information. Moreover, creditcard companies may also have past purchase history information collectedfrom their members' interaction with specific merchants and specialinterest areas, such as interest in electronics, music, sports, etc. Thelevel of detail of the members' information depends on the nature of theuser network.

The member segmentation module 220 processes the member users' databased on the demographic and preference data using eligibility rules andexisting marketing models to create a market segmented member database320. In particular, for descriptive purposes only, each horizontal rowof the segmented member database 320 represents members associated witha particular market segment based on their demographic and preferenceinformation. As explained further below, this segmentation isdynamically adjusted as the users' preference information changes over aperiod of time. The initial segmentation is made based on rules andmodels applied to the information provided by the networks.

Member Enrollment

To create more accurate segments of members, the members are directed toaccess program enrollment module 230 through an enrollment website, forexample, to create, update, and/or supplement the members' informationobtained from the user network. Additional and/or missing demographicand preference data may be collected through the program enrollmentmodule 230 to supplement the data from the member user database 210. Thegathered preference and demographic data are stored in the userpreference data store 240 a and the user demographic data store 240 d,respectively.

Pilot Groups

Once the segmented member database 320 is populated, a pilot group 330of the segmented member database 320 is generated. The pilot group 330is a sampling of members across all the market segments (denoted in thevertical dotted line) to obtain a workable subset that accuratelyrepresents the entire collection of members. This pilot group 330 isprocessed to create initial rules and member cells to be tested andverified as accurate representation of the members' personas, theprocess of which is explained further below. Once the sampled set ofmembers in the pilot group 330 has been validated, the determined rulesand personas are applied to the entire body of members.

As shown in FIG. 4, a sample subset 400 of the pilot group 330 is usedto create member segmentation cells 410. Member segmentation cells 410are generally categorized into three types of cells: (1) custom cells420, (2) advanced cells 430, and (3) basic cells 440. Custom cells 420refer to member segmentation categories with rules customized inaccordance with the directions from the affinity group or network forselect members. For example, a credit card company may want tocategorize certain segments of its members as “luxury travel” members.As another example, an employer may want to categorize certain segmentsof its employees as “officers.”

Advanced cells 430 are directed to member segmentation categories andassociated rules generated by the narrowcasting system 38. Advancedcells 430 are created based on demographic, preference, and behavioraldata collected about the members. For example, a certain combination ofdemographic, preference, and behavioral data suggests that a particularsegment of member users are “workaholics” while another segment ofmember users are preparing for a “wedding.”

Basic cells 440 refer to member segmentation categories generated frombasic demographic information. For example, one basic cell may bedesignated as “males” in the “national” region while another basic cellmay be designated as “females” in the state of “New York.” Specificsegmentation cells are generally created based on the needs of thenetwork and the benefit services it wants to provide for its members.However, segmentation cells may be created as marketing testing beds formerchants requesting to move certain types of items, for example. Thisprocess will be explained in further detail below.

Once the member segmentation cells and associated rules have beendetermined, the members in the sampled subset 400 of pilot group 330 inthe segmented member database 320 are assigned to appropriate membersegmentation cells. A member user may be associated exclusively to aparticular cell or end up being associated to multiple cells based onthe segmentation rules. The member segmentation cells and rulesassociated thereto are stored in rules/personas data store 240 c (FIG.2).

Offer Segmentation

The member segmentation cells 420, 430, and 440 have associated rulesthat decide which offers in the offer database 260 will be associatedwith which segmentation cell. As explained above with respect to FIG. 2,the offer database 260 is populated with various types of offers fromhosted merchants 260 a and network-affiliated merchants 260 b. Asexplained above, network-affiliated merchants 260 b may be groups ofmerchants that provide unique offers to members of particular networksdue to their affiliation to the particular network. For instance, aparticular employer may have a negotiated discount with a specificclothing store for its employees to encourage loyalty to that particularclothing store. As another example, a particular credit card company mayhave negotiated incentives with a particular merchant if the member useruses the particular credit card to transact with the merchant. Othermerchants who wish to use the narrowcasting system of the presentinvention to provide various offers to the member users are generallyclassified as hosted merchants 260 a with no particular affiliation tothe user networks.

The offers in the offer database 260 undergo offer segmentation based onrules stored in the rules/persona data store 240 c. As shown in FIG. 5A,various rules may be applied to segment the offers in the offer database260. For example, the offer segmentation rules may include suppressionrules 510, designation rules 520, and offer mix integration rules 530.In particular, the networks may have reasons to exclude certain offersfrom being made available to their members. Some examples of suppressionrules 510 may include suppressing certain offers from being assigned tospecified segmentation cells by categories (e.g., offers related to“flowers”), by merchant (e.g., competitors), by offers (e.g., “instantrebates,” “free shipping”), and other suppression criteria requested bythe user networks. Conversely, networks may have reasons to specificallydesignate certain offers to be made available to their members. Someexamples of designation rules 520 may include offers based on strategicrelationships, business partnerships, and other designation criteriarequested by the affinity groups/networks. The offer mix integrationrules 530 determine which offers are to be mixed together to form anoffer mix. For example, offer mix integration rules 530 may designatecertain hosted offers, network exclusive offers, and jointly sourcedoffers into an integrated mix of offers. The offer mix resulting fromthe offer mix integration rules 530 are segmented and designated tospecific cells in the offer segmentation module 540.

More specifically, the narrowcasting engine 250 (FIG. 2) matches variousoffer mixes resulting from the offer mix integration 530 with the membersegmentation cells 420, 430, and 440 based on the rules assigned to eachcell stored in the rules/persona data store 240 c (FIG. 2). FIG. 5Bshows exemplary offer segmentations matched with some of the exemplarymember segmentation cells.

Branding

To draw interest and make an impression of relevance to the members whowish to enroll and eventually use the programs created by thenarrowcasting system of the present invention, the narrowcasting system38 provides branded site customization for each member based on theiraffiliated network and assigned segmentation cells. The narrowcastingsystem 38 applies branding strategies based on a two-phase approach. Forexample, as shown in FIG. 6A, Phase 1 block 610 compiles results fromprimary research to leverage members of the segment and/or segmentexperts to obtain informed branding. Research is conducted by posingvarious preference questions to member users associated with the membersegment for which the branding strategy is being built. Some examples ofprimary research resources include feedback from segment members,information from industry experts, and opinions from focus groups.

In Phase 2 block 620, results from secondary research are compiled toaugment and validate primary research results. Some examples ofsecondary research resources include data/analytics, market research,industry periodicals, and inspiration screens from other websites. Theresults of Phase 1 and Phase 2 are combined to obtain a brandingstrategy 630. FIG. 6A shows an example of the branding approach usingthe “Weddings” member segment. As shown in FIG. 6B, the obtainedbranding strategy for a member segment (e.g., “Weddings”) is associatedwith a segment cell. Thereafter, the obtained branding strategy isapplied and provided to members who are associated with the segmentcell.

CABR (Credibility, Affinity, Benefit, and Redemption)

Once members and offers have been segmented, the narrowcasting system 38tests the segmentations using the active learning approach. The activelearning approach according to the present invention includes a twophased approach. As shown in FIG. 7, the first phase (Phase I) includestesting a sampling of members and teaming their response to the offersegmented mix developed for the segments associated to the testedmembers. The responses are analyzed and results are “learned” forrefinement. The results of Phase I are applied to Phase II whichincludes launch of the programs and initiatives developed by thenarrowcasting system using real-time optimization and feedback loop.

FIG. 8 shows Phase I of the active learning process according to thepresent invention. In the test planning stages of Phase I, test membersegmentation, offer segmentation, and CABR messages are developed foreach segment of the test members. “CABR” stands for credibility,affinity, benefit, and redemption. “Credibility” messages focus on thenetworks' value-proposition, business model, and/or third partyvalidation (e.g., review articles from reputable entities). “Affinity”messages focus on the relationship between the members and theirnetworks. “Benefit” messages focus on value-added offers. “Redemption”messages focus on how to redeem the offers emphasizing on ease,time-savings, and convenience. The following are examples of CABRmessages directed to the same benefit program:

Credibility—“You already enjoy the many benefits that only [Network]members have access to, now you have another—the ability to save 10%-70%at your favorite brand name merchants every day through the [Network's]program.”

Affinity—“Because you are a valued [Network] member, you're eligible toenjoy savings of 10%-70% at your favorite brand name merchants everyday, as well as gain access to private events and product launchesthrough the [Network's] program.”

Benefit—“Visit the [Network's] program today and save 10%-70% at yourfavorite brand name merchants every day.”

Redemption—“Save 10%-70% at your favorite brand name merchants everytime you purchase—simply by making your purchases through the[Network's] program.”

CABR messages are messages that emphasize one of the four categories todetermine what type of messages the users in each of the segmented cellsrespond to more readily. For instance, users in a particular segmentedcell may respond to messages geared towards “credibility” and“redemption” while users in a different segmented cell may respondbetter to messages geared towards “affinity” and “benefit.”

Once the test planning is complete, the developed CABR messages are sentto the test member segments with various offer mixes determined from theoffer segmentation in the calibration stage. The purpose of thecalibration stage is to gather response data from the test membersreceiving the CABR messages. Once the responses have been gathered andanalyzed, a second CABR message is sent with different offer mixes inthe validation stage. The purpose of the validation stage is to verifythe results of the analysis gathered during the calibration stage and tofurther refine the CABR messages based on the responses during thevalidation stage. FIG. 9 shows in more detail examples of thecalibration and validation CABR messaging.

In the last stage, all of the data gathered through the calibration andvalidation stages are analyzed and summarized. The information learnedduring the testing stages is then incorporated into the launch of themessages to all of the members. Depending on the learning, launchmethodology is tailored for each member segment. For instance, FIG. 10shows an example of launching email messages to the members in segmentsA-J based on the learning. As shown, messages related to launch ofmessages for members of segment A may include a teaser, invitation 1,and invitation 2 in successive periods. For members of segment B, theteaser and invitation 1 messages are sent in successive periods with adelayed invitation 2 message by one period.

In this way, the narrowcasting system 38 dynamically gathers, analyzes,and adjusts the effectiveness of each narrowcasted message. As shown inFIG. 11, the narrowcasting system of the present invention uses theactive learning process in a continuous feedback loop to build/launch,gather, analyze, and refine each narrowcasted message such that the nextmessage is more accurate and effective in eliciting responses from theusers.

“Active Data Gathering” (ADG) and “Active Learning” (AL)

Having described some of the components of the narrowcasting system ofthe present invention, the narrowcasting communications in accordancewith the present invention is implemented using a two-prong approach totarget the right product to the right person at the right time: (1)active data gathering, and (2) active learning model. To this end, usingartificial intelligence, for example, the narrowcasting engine 250 (FIG.2) includes an active data gathering ADG and active learning AL thatperforms the active data gathering process and the active learningprocess to obtain future buying data of each member.

As shown in FIG. 2, the active data gathering includes three aspects:(1) data quality 251, (2) trust 252, and (3) data availability 253. Thedata quality 251 is directed to the effectiveness of particular dataelements in regard to predicting future buying behavior (i.e., “forwardlooking” data). For example, reminder data has the highest data quality,while behavioral click data is of a lower quality.

The trust 252 is directed to the quantity of preference data per userand works to increase the amount gathered per user. As used herein,“preference” data refers to data that is communicated by the user toindicate future buying preferences. The most trusted communications arethose that have been specifically requested by the user. Therefore,preference data are the highest quality data to determine the mostrelevant offers for the user. For example, reminder data is used to sendemails to members, alerting them to the offer that they asked to bealerted about. Untargeted marketing emails diminish trust, in that theuser may stop believing that giving preference information will resultin a more relevant and customized experience. For this reason,preference data is preferably gathered continually so as to maintaintrust. One process used to gather preference data is called “intelligentquestioning.” Intelligent questioning, described in further detailbelow, uses algorithms from the active learning AL to infer a user'spreferences and gives the user an opportunity to confirm thoseinferences. For example, the active learning AL may infer that aparticular user is likely to be interested in purchasing at a particularmerchant. The narrowcasting engine 250 will confirm this inferredpreference by dynamically presenting the user with a preference question(e.g., a reminder) about that merchant and detecting the user'sreaction.

The data availability 253 is directed to evaluation of raw data andensures that the data is normalized for marketing purposes. For example,reminder data is binary (i.e., the user accepts or rejects the offeredreminder) and therefore can be easily used, while suggestion and searchdata (e.g., names of products, merchants, etc.) must be normalizedbefore use.

The active learning AL includes three aspects: (1) targeting management254, (2) fatigue management 255, and (3) content optimization 256. Thetargeting management 254 is directed to control and tracking of responserates to various algorithms for inferring a user's interest in aparticular merchant. Important factors in generating high response mayinclude recency of data and customer type. Recency is a rating of how“old” the data is. The more recently the data is collected, the higherquality the data is and the stronger the response rate. Customer type isa classification of the shopping patterns of a user. For example, “typeA” customers may be defined as people who are infrequent shoppers butspend a large amount when they do shop. “Type B” customers may bedefined as people who shop frequently but spend less during eachpurchase. Based on the customer type, a user will be marketed toaccordingly. Additional factors may be incorporated into the targetingmanagement 254, such as behavioral and demographic data. The fatiguemanagement 255 is directed to monitoring of response rates of individualusers and alters the frequency of communication to that user. Thecontent optimization 256 is directed to testing and monitoring ofresponse rates of users to messages with different CABR positioningsdescribed above. These aspects of the active learning module AL combineto optimize response rates.

For example, a typical “type A” customer may be an investment banker anda typical “type B” customer may be a bank teller. The investment banker(type A) is extremely busy and has little time available to readmarketing messages. Accordingly, the fatigue management 255 is used tolimit the quantity of emails to this user to only the most relevantofferings resulting in infrequent, but highly responded to emails. Thebank teller (type B), by contrast, will receive more frequent andconsistent emails, as they tend to enjoy reading the messages and enjoya variety of offers. The content optimization 256 is used to determinethat the “Credibility” and “Redemption” messages (from the CABRframework described above) are most effective for “type A” as thesecustomers desire assurance of quality and a fast redemption while“Benefit” messages are most effective for “type B” as these customersare frequent shoppers with the knowledge and time to price-compare. Inthis manner, the active learning AL “learns” to optimize response ratesfor individual users.

Communication Management

The communication management CM is directed to managing the schedule ofthe narrowcasted communications, such as email and websitecommunications sent to the users. The communication management CM isused to interface with the active learning AL to match users and offers,for example, and is used to interface with the active data gathering ADGto determine what additional preference data should be gathered from aparticular user, for example.

FIG. 12 illustrates the data gathered during the active data gatheringprocess according to the present invention. As shown in FIG. 12, theactive data gathering process of the present invention includesobtaining data about each member provided by the networks and themerchants obtained directly from the members. These explicit datainclude information such as names, physical addresses, email addresses,gender, age, and specified preference information that is obtainedduring registration and completed transactions. The active datagathering process also includes obtaining data inferred from members'activities on the narrowcasting system of the present invention. Forinstance, gender and location information may be inferred based onactivities and choices made by the members while accessing theirportals. For example, if the user searches for items and offersgenerally attributable for males (e.g., men's clothes, men's shoes,power tools, electronic gadgets, etc.), the narrowcasting engine 250 mayinfer that the user is a male. If the user searches or selects items andoffers from walk-in stores in a particular region, the narrowcastingengine 250 may infer that the user lives in that particular region. Inthis example, it is possible that the user is a female in one region whois looking for a gift item for a male who lives near the stores ofinterest. Accordingly, the active data gathering process according tothe present invention is performed on a continual basis, constantlyupdating the users' activities to modify the explicit and inferred datato obtain an accurate profile. As more information about the user isgathered, the information presented to the user, including preferencequestions and offers, is refined to be more relevant to the user. As theinformation presented to the user becomes more relevant, the user isinduced to provide more information about the user as the user willspend more time viewing and selecting the information presented, thustriggering even more preference data gathering about the user.Therefore, this feedback loop continues to allow a significant amount ofpreference data gathering of the user.

FIG. 13 illustrates the active learning model according to the presentinvention. The narrowcasting system of the present invention takes thetraditional marketing approach used by networks, which traditionallyidentifies “who” the buyers are, and approaches used by the merchants,which traditionally identifies “what” the consumers buy, and optimizesthe effectiveness of a marketing campaign by identifying “why” a buyerpurchases a particular product.

In particular, as shown in FIG. 13, networks typically collectdemographic data and cluster like-minded individuals based on theirdemographic data (i.e., “demographic clusters”). Accordingly, thenetworks use demographic-based algorithms to target information to theusers. On the other hand, merchants typically collect transactional dataand cluster like-minded individuals based on their transaction data(i.e., “transaction clusters”). Accordingly, the merchants usetransaction-based algorithms for their targeted marketing initiatives.In contrast, the narrowcasting system of the present invention combinesthe demographic clusters from the networks with their marketing response(i.e., “marketing response clusters”) to increase the response frommarketing initiatives from the right customer. Additionally, thenarrowcasting system of the present invention combines the transactionclusters from the merchants with the marketing response clusters totarget the right product for profitability.

Moreover, the narrowcasting system of the present invention leveragesthe demographic clusters and the transaction clusters with the marketingresponse clusters to create “personas” that target the right product tothe right customer at the right time. The active learning module ALleverages known user data (i.e., preference, behavioral and demographicdata) to infer a particular user's future buying preferences. Forexample, based on personas and other segmentations, it is inferred thata certain user, such as a member of the persona or segment, will haveinterest in a particular merchant. To confirm this interest, the activedata gathering module ADG may dynamically present the user with apreference question (e.g., a reminder) or with an offer (e.g., aprominent link on the website portal or email) and monitor if the userresponds. In this mariner, the system uses behavioral data to infer auser's future buying preferences and uses the website to confirm thatinterest, generate more preference data, and refine the algorithm.

Communications

As shown in FIG. 2, based on the results of the active data gatheringmodule ADG and active learning module AL, the narrowcasting engine 250controls two types of communication: direct and indirect communications.Direct communications refer to narrowcasted communications, such asemails, based on preference data (i.e., “forward looking” data). Allcommunications sent by the narrowcasting engine 250 are targeted, butdirect communications, such as emails, leverage the highest qualitydata—i.e., preference data—which is most predictive of future purchasingbehavior. These messages build trust, as they are “customer service”driven (i.e., responding to a users request) and help to generate morepreference data. Indirect communications are communications, such asnewsletters and website personalization (i.e., website placements) thatare based on behavioral data (i.e., “looking back” data). Indirectcommunications may be used to drive users to the portal website andcapture additional preference data.

Preference Building

The narrowcasting system of the present invention collects and buildspreference profiles of a user in a continuous, dynamic process. Thenarrowcasting system of the present invention employs several mechanismsfor collecting and building user preference profiles. As alreadydescribed above, the narrowcasting system of the present inventioninitially obtains highly specific and relevant information from thenetworks who register with the narrowcasting system of the presentinvention to host benefit programs for their members (i.e., networkmember database 210). As briefly described with reference to FIG. 2-4above, building the initial preference profile of the member useraccording to the present invention begins with member user's data thatis already comprehensive and reliable.

As a preface to describing the preference building aspect of the presentinvention, it is important to note that because users of the presentinvention are members of the registered networks, the users' demographicdata are more detailed and reliable than those collected by prior artsystems. Some examples of networks include employers, financialinstitutions, such as banks, lenders, and credit card companies, andother trusted groups, such as trade groups (e.g., American AutomotiveAssociations) and institutional organizations (e.g., American BarAssociation). Because the narrowcasting system of the present inventionprovides network members access to offers from hosted/affiliatedmerchants and service providers, the network provides member informationto the narrowcasting system of the present invention to provide benefitservices for their member. Accordingly, the narrowcasting system of thepresent invention begins with data that prior art systems strive toobtain.

As described above, member information provided by the networks isanalyzed and segmented and stored in the user preference data store 240a and the user demographic data store 240 d along with membersegmentation rules stored in rules/personas data store 240 c. Thenarrowcasting engine 250 uses the data in the user preference data store240 a, the user demographic data store 240 d, and the rules/personasdata store 240 c to create initial member segmentation cells and offersegmentations. Therefore, even if a user who enrolls with thenarrowcasting system of the present invention without providing anyfurther information, the user receives highly relevant offers to theuser from the moment the user activates his or her account.

In addition to the information provided by the networks, thenarrowcasting system uses the registration/activation process to obtaineven more relevant information about the users. As described in detailbelow, the narrowcasting engine 250 uses the initial data about the userstored in the user preference data store 240 a and the user demographicdata store 240 d to generate questions to refine/supplement informationabout each user who enrolls with the narrowcasting system. This“intelligent questioning” process allows the narrowcasting engine 250 tovalidate, modify, and/or refine the member's data. The informationcollected during the registration/activation process is added to theuser preference data store 240 a and the user demographic data store 240d, and member segmentation cells and associated rules stored in therules/personas data store 240 c are refined. Thus, while prior artsystems begin the collection of user information during the registrationprocess, the narrowcasting system of the present invention uses theregistration process to verify and/or supplement the information alreadystored in the user preference data store 240 a and the user demographicdata store 240 d.

In particular, FIG. 14 shows a more step by step view of theregistration/activation process. As shown in FIG. 14, a user (alreadyassociated with a member segment cell) accesses the network programportal 280 a, 280 b (FIG. 2) to register and activate the benefitsprogram. If the user is responding to an invitation message, the portalmay be accessed via a link embedded in the invitation. If the user isalready enrolled, the network program portal (280 a, 280 b) may beaccessed manually by typing in the assigned address to the portal or viapre-established links on the users' computer, such as through anIntranet site. When the user logs in for the first time, the user isguided through an initial preference building process. In reality, asdescribed above, the narrowcasting system of the present inventionalready has preference data initially provided by the networks stored inthe user preference data store 240 a and the user demographic data store240 d. However, to the user, the process of identifying preferences atthis stage is a first time for the user. Therefore, while the preferenceselection process during activation may be perceived as an initialpreference setup to the user, in reality, the preference buildingprocess during registration for the narrowcasting system of the presentinvention is a process to refine the preference profile for theparticular user.

To engage the member user to assist in refining the preference buildingprocess, the narrowcasting engine 250 begins the intelligent questioningprocess by presenting the user with various questions through the portal280 a, 280 b. The questions are dynamically generated to becell-specific to the user, the cells being assigned during the membersegmentation process as shown in FIG. 4. The types of questions may bedichotomous questions (e.g., “yes” or “no”), multiple choice questions(e.g., selection from a set of answers), rank order questions (e.g.,rank a list of answers based on level of interest), or multiplechoice/battery matrix questions. Other types and methods of presentingthe questions may be used without departing from the scope of thepresent invention. For instance, instead of directly asking questions,the questions may be offered as an interactive interface, such as agame, to input the member user's preference selections. Because thenarrowcasting engine 250 has access to the initial preference data forthe user as stored in the user preference data store 240 a and theinitial demographic data stored in the user demographic data store 240d, the questions presented are preferably designed to be more specificto the user than broad questions generally employed by prior artsystems, thereby presenting less but more pertinent questions about theuser.

For instance, FIG. 15 illustrates examples of the type of customizedquestions generated for users based on their member segmentationprofiles. As shown, members in the network member database 210 aresegmented into the relevant markets presented by their initialpreference information. From the segmentation information, varioussegmented cells 410 (custom cells 420, advanced cells 430, basic cells440) are created and associated with each user. When a user logs in toregister/access the benefit page through network program portal (280 a,280 b), the user is presented with dynamic questions related to theuser's associated cell. As described below, these preference questionsmay be asked during registration/activation of the user's benefit siteas well as during continual use of the site. All of the informationprovided during the registration process is then stored in the userpreference data store 240 a and the user demographic data store 240 d.

FIG. 16 shows an example of a preference building page engaging the userin intelligent questioning. As shown in FIG. 16, various questions arepresented to the user. These questions are dynamically created for thespecific user based on the information already obtained from the user'snetwork in setting up the account. In addition, this page also displayssome of the offers available to the user “waiting inside.” Again, thesepresented offers are dynamically created for this particular user basedon the information obtained from the network. As the user begins toanswer the questions, the presented offers dynamically change based onthe user's answers. For instance, as shown in FIG. 16, if the userselects “buying a new home,” the offers shown on the left will change toinclude items related to homes (e.g., offers related to mortgages,furniture, etc.). Moreover, if the user moves the cursor over to one ofthe offers displayed, the offer pointed to by the cursor enlarges theoffer to display specific information regarding the offer (e.g.,jewelry, shown in FIG. 16). During this time, the narrowcasting engine250 keeps track of all the activities being performed by the user. Forexample, the narrowcasting engine 250 may keep track of which offers theuser appears to be interested in by tracking the offers viewed and howlong the offer is viewed (e.g., by measuring the time of the cursorhovering over a particular offer to view the details). The narrowcastingengine 250 may also keep track of the answers beingselected/de-selected. All of the collected information is used to add,modify, or refine the information about the user already stored in theuser's preference data store 240 a, the user's behavioral data store 240b, and the user's demographic data store 240 d.

Once the member user has been given the chance to designate his/herpreferences, the member user is given access to the benefits site. Thebenefits site is dynamically generated by the narrowcasting engine 250customized for the user based on the user's associated member segmentcell generated from the preference data provided by the network. If theuser has provided further information during the registration process,whether explicitly (e.g., answers to the questions) or implicitly (e.g.,by hovering over an offer), the narrowcasting engine 250 dynamicallyadjusts the user's associated preferences based on the informationprovided and dynamically adjusts the offer mix to be presented to theuser. In this way, the narrowcasting system of the present inventioninstantly provides relevant offers from the first time the user accessesthe benefit program hosted by the narrowcasting system.

Once the preference data provided by the networks and by the user duringthe registration/activation process is processed, the narrowcastingsystem of the present invention tracks users' activities throughout theuser's access to the narrowcasting system to further collect and analyzepreference information of each user. The three types of data collectedby the narrowcasting system of the present invention are demographicdata, preference data, and behavioral data. For exemplary purposes only,the preference data is stored in the user preference data store 240 a,the demographic data is stored in the user demographic data store 240 d,and behavioral data is stored in the user behavioral data store 240 b.As already described, the information may be stored in separatedatabases or stored in different portions of the same database withoutdeparting from the scope of the invention. The demographic dataincludes, but is not limited to, home and work locations, gender, incomelevel, job title, and marital status. The data may be obtained fromemployee data files. Preference data includes, but is not limited to,current and future purchase decisions obtained from user suggestions,requests, and selections. Behavioral data includes, but is not limitedto, shopping habits and purchasing behavior over a period of time.

As described above, all of the contents presented to the user aredynamically generated and tracked in a real-time, continuous feedbackloop. In general, the intelligent questioning during the active datagathering process includes reminders, searches, suggestions, andcalendaring features. The reminder feature notifies users of up-comingoffers or missed offers and asks whether the user would like to bereminded of the offer or similar offers in the future. For instance, ifthe user missed a 2-hour special sale or promotion, the user can requestthe system to notify the user if another or similar offer comes up inthe future. If there is an offer a month away that the user does notwant to miss, the user may request a reminder prior to the offer (e.g.,days, hours, or minutes before the offer takes effect). To facilitatethe notification of offers, the narrowcasting system of the presentinvention displays offers (past, present, and future) on a calendar suchthat the user can view important offers at a glance. The reminderfeature may be connected to the calendar feature to optimize theopportunity for the user to interact with the system.

The search feature allows the user to search for specific offersavailable to the user. The user may search for offers specific to aproduct or category of products, merchant or type of merchant, time, andthe like. The specific search parameters may be varied without departingfrom the scope of the present invention. All aspects of the searchperformed and the results viewed are stored as preference data.

The suggestion feature allows a user to suggest specific merchants,products, and/or services not found in the search to be added to thenarrowcasting system of the present invention. The suggestions made bythe user are also stored as preference data. The users' activitiesrelated to all of these features are collected and analyzed to validate,update, modify, and/or refine the users' preference and behavioral data.

Data Collection

FIGS. 17A-17G illustrates an example of how these different types ofdata are collected. FIGS. 17A and 17B show activities of a user who isresponding to an invitation to register and activate the benefit programhosted by the narrowcasting system of the present invention for theuser's network. At the time the user is taken to the registration pagethrough the network program portal (280 a, 280 b), the user'sdemographic information is already in the user demographic data store240 d as provided by the network. As described above, the registrationprocess may be used to add or update any of the demographic information.FIG. 17B shows that once the user logs onto the portal, the user isinvited to select his or her preference of interest as well asadministrative items (e.g., communications channel). The preferenceinformation provided by the user is then stored in the user preferencedata store 240 a as preference data.

FIGS. 17C and 17D show a user beginning to use the benefit program. Forinstance, the user may start searching for particular items and offersfrom a particular merchant stored in the offer database 260. The searchterms used in the search are tracked and stored as preference data asshown in FIG. 17C. In addition, any selections made by the user asshowing interest in particular offers from particular merchants are alsostored as shown in FIG. 17D. FIG. 17D also illustrates the calendar andreminder features discussed above.

FIGS. 17E-17G show collection of user's behavioral data during activityon the benefit site. For instance, FIG. 17E shows that the user selecteda particular offer (e.g., shoes) from a particular merchant (e.g., shoestore) to view further information about the offer (e.g., Interest Level1). This information is stored in the user behavioral data store 240 b.FIG. 17F shows that the user, upon viewing a detailed description of theoffer, adds the item in the offer to the shopping cart, for example(e.g., Interest Level 2). This information, again, is stored in the userbehavioral data store 240 b. FIG. 17G shows the data stored in the userbehavioral data store 240 b after the user has viewed, selected, andclicked to a particular item for purchase (e.g., Interest Level 3) froma particular merchant (e.g., an engagement ring from a jewelry store).This information may include the actual purchase of the item. Asdescribed, the narrowcasting system of the present invention not onlycollects demographic and preference data about a user before and duringactivities on the benefit site, but the narrowcasting system of thepresent invention also collects behavioral data to build an accurateprofile of each user to be used by the narrowcasting engine 250 topresent relevant offers to the users at relevant times.

In addition to collecting and building demographic, preference, andbehavioral data, the narrowcasting engine 250 analyzes the informationto determine correlations and most pertinent variables. As an example,as shown in FIG. 18, the narrowcasting engine 250 is able to analyze thebehavior of males and females in a particular demographic profile.According to the analysis, the most highly-correlated items for high-endflower purchasers are flat-screen televisions and luxury suits, whilelocation of the correlated purchasers does not appear to be significant.Such highly-correlated information generated by the narrowcasting engine250 is used to create rules for the segmented cells 410 as well asanalysis reports, explained in detail below. Throughout the datagathering process, the narrowcasting engine 250 collects not only theexplicit data provided by the networks, users, and merchants, butcollects inferred data as well to supplement the explicit data to moreaccurately predict the users' preferences. As briefly mentioned above,inferred data refers to information about the user that is inferred fromthe explicit data gathered. For example, if a user does not furnishwhere the user lives, the explicit data gathered about the user, such asrepeated transactions at a particular store, is used to infer that theuser lives near the store. Moreover, the preference data gathered by thenarrowcasting system of the present invention represents future buyingdata rather than past purchase histories and behaviors characterized bythe prior art systems.

Personas and Life Events

The demographic data, preference data, and behavioral data collected foreach user in accordance with the present invention allows fornarrowcasting engine 250 to present highly relevant information to theusers at relevant times. As described above, the narrowcasting engine250 provides relevant information by segmenting the members and offersinto associated segment cells 410 and delivering the offer mix 530 tothe users associated with the specific segmented cells 420, 430, and440. As described above, members of custom cells 420 are segmented inaccordance with specified parameters from the network. That is to say,custom cells 420 and the rules associated thereto are customized inaccordance with the networks' requests. Members of basic cells 440 aresegmented in accordance with basic demographic data (e.g., gender andlocation, such as males living in New York). Basic cells 440 are used as“targeted broadcast” communications by the narrowcasting system of thepresent invention. For example, basic cells 440 may be defined by rulesused to send communications to users across networks or to largesegments for newly provided programs and services. The targeted userscan be narrowed or broadened based on the demographic data selected asthe communications criteria for the segmented members for the cell.Members of the advanced cell 430 are segmented based on “personas”and/or “life events,” as further explained below.

A “persona” is a synthetic personality and rules associated theretobased on specific demographic, preference, and behavioral data, allwithin the context of time. More specifically, an individual's behaviorand preferences, especially related to purchasing habits, are related toa specific time period in the individual's life. For example, anindividual who is single and at the beginning stages of his or hercareer will exhibit a particular purchasing behavior that is differentfrom the behavior of an individual who is established in his or hercareer and is newly married. An individual who has recently had a babywill display still yet a different purchasing behavior than the othertwo individuals. The “personas” generated by the narrowcasting engine250 are based on the premise that an individual will be in a particularlife style for a finite amount of time. By detecting particular triggerevents (based on preference and/or behavioral data—e.g., purchase of anengagement ring, purchasing a home, purchasing a minivan, etc.) andobserving the following purchase preferences and behaviors, a moreaccurate profile, and eventually future purchasing data, can beobtained.

Accordingly, a persona in accordance with the narrowcasting system ofthe present invention is characterized by a defined event, a trigger ofthe event, duration of the event, and the user's location in thetimeline of events. In particular, the personas generated by thenarrowcasting engine 250 relate to the “directional” (i.e., future trendof purchases) of the user in his or her purchasing behavior rather thanthe “data point” (i.e., item of purchase) of the user's purchases.

As an example, a “workaholic” persona is defined by rules that look forusers who work in a particular industry, have a high level of education,and have an income above a particular threshold. “Workaholics” tend topurchase expensive items such as high end electronics and jewelry andtend to travel frequently. FIG. 19 shows a fictitious user who issegmented into a “workaholic” cell. Accordingly, rules developed for the“workaholic” cell are used to create offer segmentation for theworkaholics, and those who are segmented into the workaholics cell arepresented with the appropriate offers relevant to this persona. Thenarrowcasting engine 250 segments the offers based on the workaholicpersona and delivers the most appropriate offers to those in this cell,such as offers related to flat screen televisions and diamond jewelry,as shown in FIG. 19.

Similarly, “life events” are cells segmented based on demographic,preference, and behavioral data that indicate a particular stage of lifethat the user is in. For example, a “wedding” life event is defined byrules that look for users who are single or divorced, who recentlyresearched and/or purchased engagement rings, or viewed offers fromwedding dress vendors. These patterns indicate that the user may beplanning for a wedding that may occur in a short period of time. Anotherexample may be a “baby” life event that is defined by rules that lookfor users who are married (or users who fit the “weddings” profile) whorecently researched and/or purchased baby necessities. These patternsindicate that the user may be expecting a baby in a short time.Accordingly, the offers segmented by the narrowcasting engine 250according to the life events may include travel offers for theirhoneymoon or offers for family-friendly vehicles, such as minivans.Therefore, the narrowcasted communications according to the presentinvention proactively communicate offers that are relevant and timely.Prior art systems, by contrast, are reactive, and thus present offersthat are irrelevant and or too late to be of use to a user.

In each case as described above, a defined event (e.g., new job,wedding) is detected based on a trigger of the event (e.g., businessapparel, engagement ring). Using data gathered from other individuals, aduration of these events can be approximated (e.g., 1-5 years for theworkaholic, 6-12 months for the wedding). The individual's positionwithin this time frame can be determined based on the preference andbehavior data (e.g., luxury car may indicate the latter stages of theworkaholic while purchase of a wedding gown may indicate the weddingdate is near). By using these established personas, future purchasingdata can be determined and appropriate offers (e.g., vacation orhoneymoon packages) may be presented to the appropriate individuals atthe most relevant times in their life stage.

Reports

The narrowcasting engine 250 dynamically tracks, collects, and updatesall data that is communicated between the users and the merchants. Inaddition, the narrowcasting engine 250 analyzes the responses from theuser to provide various services to the networks and/or merchants hostedby the narrowcasting system of the present invention. From the networks'perspective, the offers provided by the affiliated merchants 260 b aregenerally based on contractual terms that provide financial incentivesfor both parties. Therefore, results of the users' activities and anytransactions that results need to be accounted for. Moreover, if thenetwork is a financial institution, such as a credit card company,analysis of user responses and activities may be used to pursue thenetworks' own marketing campaign to draw more members.

From the merchants' perspective, affiliated merchants 290 are interestedin the users' activities/transactions as well as the other side of thecontractual obligations with the networks hosted by the narrowcastingsystem of the present invention. Moreover, whether the merchant is anaffiliated merchant 260 b or just a hosted merchant 260 a, the merchants260 a, 260 b may be on a contractual relationship with the narrowcastingsystem of the present invention for the offers made to the users.Additionally, analysis of user activities and responses may providevaluable marketing information that may be important in developing themerchants' own marketing campaigns.

The narrowcasting engine 250 provides, but is not limited to, thefollowing levels of analysis and corresponding reports to the networksand/or merchants: (1) Basic, (2) Analyzer, (3) Forecaster, (4) ScenarioBuilder, (5) Advisor, (6) Custom.

(1) Basic—The Basic level analysis provides quarterly reports of theusers' activities on the system. The Basic report is useful in reportingof network members' activities as well as marketing campaigns, such assales reports, leads, and campaign summaries.

(2) Analyzer—The Analyzer level analysis provides weekly, monthly,quarterly, and yearly reports that provide, in addition to the Basicreport, usage by segments and leads/sales by merchant. The Analyzerreport provides historical trends. (FIG. 20)

(3) Forecaster—The Forecaster level analysis provides weekly, monthly,quarterly, and yearly reports that provide, in addition to the Basicreport, forecasts by segments, forecast leads, forecast sales, andforecast response. The Forecaster report also provides historicaltrends. (FIG. 21)

(4) Scenario Builder—The Scenario Builder level analysis providesweekly, monthly, quarterly, and yearly reports that provide, in additionto the Basic report, “what-if” scenarios, profitability analysis, demandcurves, conversions, and buy rates. (FIG. 22)

(5) Advisor—The Advisor level analysis provides weekly, monthly,quarterly, and yearly reports that provide, in addition to the Basicreport, executive level usage and activity recommendations, diagnostics,persona development, and user mappings.

(6) Custom—The Custom level analysis provides reports according tofrequency and level of detail specified by the network/merchant.

Purchase Funnel

As shown in FIG. 23, the “purchase funnel” refers to the process ofoptimizing the narrowcasted communication. The narrowcasting system,according to the present invention, tracks the marketing-to-purchaseprocess from end-to-end (i.e., “closed-loop”). In other words, thenarrowcasting system of the present invention tracks users' responsesfrom the delivery of the communication via the network (e.g., HRcommunication), site communication (e.g., web portal placements) andnarrowcasting, all the way through to the transaction (“TRN”).Accordingly, the narrowcasting system according to the present inventioncan analyze and break down all the data obtained from the user from thebeginning of the marketing initiative to the resulting purchase intogranular detail. For example, conversion rates (i.e., user's favorableresponse of a communication) are tracked from marketing toportal-website, from portal homepage to offer detail page, from offerdetail page to “go shop” (i.e., clicking to a merchant's website,coupon, etc), and from “go shop” to transaction (i.e., purchase). Inaddition, the present invention tracks these response rates formerchants, allowing the system to calculate averages for categories,subcategories, groups of similar merchants and the like. Thesecalculations are benchmarks that can be used to diagnose where problems(i.e., marketing breakdown) in the purchase funnel exist. For example, amerchant may have an offer detail page to go shop conversion of 23%, butthe average for like merchants is 45%. In addition to identifying theproblem areas, the analysis allows for the development of products andservices that are designed to fix specific steps in the purchase funnel.

FIG. 24 is a block diagram illustrating an exemplary data flow accordingto the present invention. As discussed in detail above, data collectedfrom the network, the merchants, and the user are input to thenarrowcasting engine of the present invention. The active data gatheringADG collects the data from these three sources through the active datagathering process as described above, and parses this data into theappropriate database (e.g., demographic, preference, and behavioraldatabases) in a format readily available for marketing. As discussedabove, the data is also evaluated as to effectiveness in predictingfuture buying behavior (i.e., “forward looking” data). The activelearning AL processes the collected data as described above and appliesrules and algorithms that will determine what offers to present to aparticular user (i.e., determine the right product for the right personat the right time). Based on results from the AL process, thecommunication management CM will send customer service emails (based onreminder and suggestion preference data, for example) and/ormarketing/advertising communication (based on inferred data, forexample). The communication of marketing/advertising messages occursutilizing one or more mediums: Internet (e.g., website portal),newsletter insertion (e.g., HR newsetters) and emails (emailnewsletters). The response is fed back into the system in real-time tocollect and refine the data to be even more accurate and relevant.

Example

FIGS. 25-30 illustrate a non-limiting example of the narrowcastingsystem and method according to the present invention. In particular,FIGS. 25-30 show an exemplary embodiment of segmentation and preferencegathering/learning in accordance with the present invention. As shown inFIG. 25, an existing user network (e.g., “BookStore”) wants to establisha rewards program on the narrowcasting system of the present invention.In block 2510, the current membership are grouped and segmented asdescribed above into RFM buckets. In this example, 4 target groups and15 segments have been established and analyzed, as shown in FIG. 26.These segments can be grouped based on the target group or segmentobjective. For example, as shown in FIG. 26, BookStore is interested inretaining members of the Gold and Silver segments. Thus, the incentivesand rewards for these segments can be designed around retaining thesesegments.

Once the groups/segments have been determined, a call to actionmessaging is developed and disseminated to the groups. As shown in block2520, using target group objectives and themes, CABR is used to developa message to promote the incentives (rewards), retention, and renewal ofthe members into the rewards program. Once the members, or potentialmembers, receive the messaging, they are invited to register into therewards perks program by providing a link, for example, to the rewardsperks website.

In block 2530, the user is then guided through the registration/loginprocess. FIGS. 27-29 show the preference building interface (i.e.,“preference game”) to ascertain the preference data of the user duringregistration. As described above, the right side of the screen (FIG. 27)is populated with some of the offers found “inside” (i.e., onceregistration is complete). These offers, while appearing random, areactually populated based on data already known about the user. As theuser answers the intelligent questions on the left side of the screen,the offers displayed on the right side of the screen dynamicallychanges, as shown in FIG. 29. Further, as the user interacts with someof the offers (e.g., hovers over a particular offer), the information ofthe user's interest is also gathered to build the user's preferenceprofile. FIG. 28 is an exemplary flow of the preference building processduring registration. Once the user's preference data is gathered duringthe registration process through the preference game, for example, thesegmented content and preference based content are collected anddynamically arranged on the user's website as personalized content.(e.g., FIG. 29).

Based on the demographic, behavioral, and preference data gatheredduring the registration/prior activities, the narrowcasting system ofthe present invention migrates the analysis to lifestyle focusedmarketing. As shown in block 2540, based on the gathered data of theuser, the user is profiled into personas to determine the user'slifestyle. Focusing on the user's lifestyle segmentation, the offersavailable in the system are segmented into the most relevant categoriesfor the user, as shown in block 2550. In block 2560, the offers and thewebsite are dynamically generated and branded and presented to the userto begin usage of the BookStore's rewards perks program.

Silent Marketing

As described above, the narrowcasting system according to the presentinvention has many applications. In particular, the narrowcasting systemof the present invention in the exemplary embodiment as described aboveserves as a rewards/loyalty platform for networks while serving as atargeted marketing platform for merchants.

From the standpoint of the members, the narrowcasting system of thepresent invention provides relevant information to the users from thetime the user first accesses the user portal. Because the users aremembers of a network and the portals are setup on behalf of thenetworks, the networks provide information about their users whenregistering to be hosted by the narrowcasting system of the presentinvention. Therefore, the narrowcasting system of the present inventionbegins with information that prior art systems strive to collect over along period of time. Because the information provided to the users isrelevant from the beginning of their experience on the portal, theusers' first impression is that of credibility and trust of thecommunications provided by the narrowcasting system of the presentinvention. This reduces the number of ignored/deleted communications,such as emails and the like. More importantly, because the offers beingmade through the narrowcasting system of the present invention arebranded through the network to which the users are members, theofferings are viewed as benefits or perks from a trusted entity and notperceived as spam. As the offerings become more relevant through usage,the trust relationship is increased, thereby perpetuating the collectionof more data points to refine the users' future buying data even more.

From the standpoint of the networks, as the members find the offeringsthrough the narrowcasting system of the present invention more relevantand useful, the network strengthens the relationship with the members.Accordingly, the members' loyalty to the network increases, therebyobtaining more business.

From the standpoint of the merchants, as the offerings are matched withrelevant members, their marketing efforts become more efficient andproductive. Rather than inundating the public with offerings that maynot even get viewed, the narrowcasting system of the present inventionprovides accurate and productive results. To this end, the narrowcastingsystem of the present invention provides a “silent marketing” option asa test bed for merchants to perform market analysis of their products.

In particular, the narrowcasting system of the present invention is a“closed loop” network with proven marketing rules (e.g., personas). Theterm “closed loop” as used herein refers to a closed environment with aspecific audience and defined rules. In contrast, an “open loop” networkis the general public that has no boundaries and unspecified audience(e.g., anyone can access the network and identity is not authenticated).Because all of the potential customers in the narrowcasting system aremembers of the hosted networks that have been segmented and analyzed,products and/or marketing campaigns can be tested with accurate resultswith no public dissemination of information about the test.

For example, a merchant may wish to determine a market for a particularproduct. Traditionally, the merchant would pay for broadcastadvertisements for the product to the general public to determine themarket for the product. However, because the general public is an “openloop” network, the response is unpredictable and sporadic. Even if theproducts are sold, the data obtained is extremely diverse to determinethe marketability of the product with any accuracy. However, using thenarrowcasting system of the present invention, members have been alreadysegmented and analyzed. Accordingly, a sampling of members using thepreference, behavioral, and persona data can be generated and theproduct marketability tested. Because the narrowcasting system of thepresent invention uses the Internet and email, the results are almostimmediate. Based on the returned results, a different or larger samplecan be generated for either re-testing or validating the results.Moreover, prototype offers and other marketing campaigns can also betested to determine their efficacy.

In another aspect, if the market is too small, traditional methods fortesting marketability exhaust the test pool. In other words, the peoplethat would have found the product useful have been used up for the test.Accordingly, there is no one else to market after the testing isperformed. However, in the narrowcasting system of the presentinvention, because the audience consists of members of large networks(e.g., employers, institutions, affinity groups) and the market pool canbe segmented and sampled over members of different networks, the totalpool is not exhausted after testing. In other words, the narrowcastingsystem of the present invention creates a controlled sampling of amarket in a controlled environment to obtain accurate results that canbe re-tested without exhausting the marketable pool. This results in theability to run additional tests without exhausting the market pool forthe actual marketing campaign.

The narrowcasting system of the present invention can be used in thisfashion to test marketability of products, cost valuation of products,effectiveness of marketing strategies, and other valuable marketabilityanalysis in a controlled, efficient manner with near instant results.Moreover, because of the effectiveness of the narrowcasting system ofthe present invention, merchants can also use the narrowcasting engine250 to move surplus products more effectively.

Merchant Network Services

As discussed above, while “relevance” of offers can increase usage byincreasing trust, breadth (i.e., quantity) and depth (i.e., quality) ofproducts and services available on the system are integral to increasingusage of the system. To increase the quantity and quality of theproducts/services, the system must be capable of increasing anddeepening relationships with merchants that provide theproducts/services. Accordingly, the narrowcasting system of the presentinvention includes various merchant network modules to increase thenumber of merchants, thereby the number of products and/or services, anddeepen the relationship to increase the quality of offers for theproducts and/or services available from the merchants.

Auto-Enroll Module

A key barrier for increasing the number of merchants, thereby increasingproducts and services on the system, is the ease of enrollment with therewards/loyalty program. Typically, rewards/loyalty programs requirethat participating merchants offer discounts and/or other incentives tobe able to market to the members of the rewards/loyalty program members.This can pose challenges to mainstream merchants who do not needdiscounts to draw customers, and particularly to those offeringdiscounts that are administratively challenging to provide (e.g.,non-public or “private” offers, offers customized to specificcommunities or types of users, or any other offers that deviate fromexisting promotional plans). Hence, merchants may be dissuaded fromparticipating in reward/loyalty programs that require discounts as acondition for participation.

On the other hand, a key barrier to increasing the quality of merchantsis the challenge of increasing the number of high-end merchants (e.g.,retailers of name brand designers) who participate in therewards/loyalty program. High-end merchants can generally sell theirproducts/services at market price without any discounts since theconsumers of those products are typically affluent individuals. Hence,merchants with high quality products/services are dissuaded fromparticipating in reward/loyalty programs that require discounts as acondition for participation.

Conversely, the merchants that offer the steepest discounts and/orincentives tend to be merchants with products/services that are lessthan desirable (e.g., out-dated products, unknown/unproven products,overstocked items, etc.). Hence, typical merchants participating inrewards/loyalty programs tend to be unknown or low end merchants, whichtends to discourage users of long term usage as the quality and quantityof offers become sub par.

As shown in FIG. 31, an exemplary embodiment of the present inventionincludes an auto-enroll module 3100 that is implemented on arewards/loyalty platform such that the system does not require merchantsto provide discounts and/or incentives to become a participant. Rather,the incentives are provided by a rewards module, discussed in detailbelow, of the system of the present invention rather than by themerchants. In an exemplary embodiment, the rewards are provided out ofthe marketing spend accounts of the merchants. Hence, while the usersare incentivized to purchase from participating merchants, the merchantsdo not have to offer discounts/incentives to be a participating member.In this way, the system and method of the present invention increasesthe number of merchants, especially the high-end merchants that do nottypically join rewards/loyalty programs as discussed above. Moreover,the system and method of the present invention includes features, asdiscussed further below, to make the registration and administration ofthe participation easy and simple for the merchants to further increasethe number of merchants participating in the network.

In the exemplary embodiment, the auto-enroll module 3100 is a securecommunications module on the system of the present invention, such as anIntranet or Internet access portal as shown in FIG. 31. As shown, theauto-enroll module 3100 includes a website-like interface withinstructions to guide the merchant through the registration/enrollmentprocess. As shown in FIG. 31, the auto-enroll module 3100 includes anoffer wizard 3110 that guides the merchant through a quick and easyprocess of creating an offer to be displayed to the user of thenarrowcasting system of the present invention. The offer wizard 3100 maybe a webpage having fields that allow entry of the merchant'sinformation, the description of the offer, link to the merchant'swebsite, and merchant's logo and/or other images regarding the offer.Other types of fields and/or interface may be used without departingfrom the scope of the present invention.

The auto-enroll module 3100 also includes options for participation. Forinstance, FIG. 31 illustrates a performance based pricing (“Rev-Share”)module 3120 that allows the merchant to sign up for a budget-basedenrollment for continuous, periodically recurring offers. In particular,when the amount budgeted by the merchant for a particular offer is spentwithin a cycle set by the merchant, the offer is suspended until thenext cycle begins. The performance based pricing module 3120 allows themerchant to set/change the budget levels, set/change frequency of thedisplay of the offers, as well as other administrative tasks regardingthe offers made available to program members. The auto-enroll module3100 further includes a quick start (“Pay and Go”) module 3130. Thequick start module 3130 allows a one time payment and activation of anoffer to be made available to the program members.

FIG. 32 illustrates an exemplary flowchart describing the enrollmentprocedure. For instance, merchant 3201 accesses the auto-enroll module3210 through a website, for example, to sign up with the system of thepresent invention. (Step 1) During the enrollment process, the merchantprovides information about the merchant and the offer to be madeavailable to the program members. Once the merchant has provided all thenecessary information regarding the merchant, the offer, and the like,the merchant information is forwarded to the registered card module3220. (Step 2) The registered card module 3220 sets up the merchant withidentification information (e.g., merchant ID), type of offer includingany discount/incentive information, and stores the information into themerchant database (not shown). Once the merchant enrollment isconfirmed, the merchant's offer or offers are added to the offeradministration module 3230. (Step 4)

The offer administration module 3230 adds and/or updates the offers intothe offer database 3235 to be used by the narrowcasting engine (FIG. 2)to make the offer to the most relevant member at the most relevant time.(Step 5) If the offer does not include any discounts and/or incentivesas the merchant is not required to do so as discussed above, the systemof the present invention may add a default incentive, such as a rewardpoint for a predetermined amount spent. The default offer may be in lieuof, or in addition to, any discounts/incentives offered by the merchant.

In addition, the offer administration module 3230 sends notification,such as an email, for example, to the merchant 3201 once the enrollmentprocess has been completed. (Step 6) The notification may furtherinclude login instructions to allow the merchant access to the offeradministration module 3230 for administering the merchant's offers, asdescribed further below.

FIG. 33 illustrates a diagram describing exemplary administrativefunctions and tools of the offer administration module 3230 madeavailable to the merchants. For instance, after enrollment, a merchant3201 togs into the account manager module 3310 using the informationsent to the merchant after enrollment. (Step 1) The account managermodule 3310 allows the merchants to perform various administrativetasks, such as changing login/password information, changing merchantinformation, viewing and changing account information, and the like.Moreover, the account manager module 3310 provides various marketingtools to the merchant 3201 such as click volume data, transaction data,discount/offer redemption data, and the like. (Step 2) The marketingdata and information available to the merchant may be varied dependingon the level of service in which the merchant has enrolled. Some of thedifferent levels of analysis and data that may be made available to themerchant are described above.

Furthermore, the account manager module 3310 provides various tools tothe merchant to manage the offer or offers enrolled in the system of thepresent invention. (Step 3) In particular, the account manager module3310 includes a heat map module 3320, an offer rank module 3330, andoffer bid module 3340.

Heat Map Module

The heat map module 3320 is a tool that conveys activity levels ofvarious aspects of the marketplace to merchants using the system of thepresent invention. For instance, the merchant 3201 or a user can use theheat map module 3320 to view the most active category of merchandiseover a specified time period. (Step 4) FIG. 34 illustrates an exemplaryheat map that indicates the most popular type of merchandise beingviewed/purchased on the system of the present invention. As shown inFIG. 34, “apparel” is the most actively viewed/purchased by the memberson the system of the present invention followed by “electronics.” Whilethe exemplary heat map of FIG. 34 displays the activities ofproducts/services based on type, the parameters may be customized by theuser. For instance, the heat map may be configured to show the name ofthe most popular products rather than by product type. As anotherexample, the heat map may be configured to show activity based onmerchant name.

Moreover, the heat map may be “clickable” to show various levels ofgranularity of the information. For example, FIG. 34 shows activitybased on product type (e.g., “apparel”). The heat map module 3320 mayinclude the function of allowing the user to click on the “apparel”section of the heat map and a new heat map may be displayed showingactivity levels broken down by categories of apparel (e.g., men, women,children, etc.) or show popularity over a period of time. Moreover, theuser may click on one of these categories to generate yet another heatmap that displays popularity based on the specific type of product(e.g., shirts, pants, suits, casuals, etc.). The heat map module 3320may be configured to display any level of granularity for any type ofparameter without departing from the scope of the present invention.

Furthermore, while the exemplary heat map of FIG. 34 illustrates acolored area graph to convey levels of activity, other graphicalrepresentations may be used without departing from the scope of theinvention. For instance, FIG. 35 illustrates various exemplaryrepresentations that may be used, such as (a) heat, (b) speed/cluster,(c) sound/vibrations, and (d) color/size. These exemplaryrepresentations are meant only to provide examples and not aslimitations. Hence, other graphical representations may be used withoutdeparting from the scope of the invention.

Offer Rank Module

One of the challenges in marketing is to induce merchants to increasethe amount of “variable” marketing (i.e., offers, discounts, incentives,etc.) offered to users. Unlike “fixed” marketing fees (e.g., periodicadvertisements), variable marketing fees are often proportionate to theultimate purchase price. Hence, this generally offers a more attractiveor predictable return on investment to merchants. However, merchants areoften unwilling to increase their variable marketing spending unlessthey have a better understanding of what impact the added spending willhave on their traffic and how the change in traffic (if any) compares totheir competitors. Unfortunately, prior art rewards/loyalty systems donot enable merchants to readily compare themselves to other merchantsparticipating in the system. Moreover, prior art systems do not enablemerchants to readily change the value of their variable marketingspending (e.g., increase/decrease the offer, discount, or incentive tousers) to compete with other merchants using the system. Additionally,prior art systems do not enable merchants to analyze how their variablemarketing impacts their traffic or how their offers compare with thoseoffers from other merchants. Hence, merchants are generally reluctant toincrease the value of the discounts and/or incentives they offer tousers participating in the rewards/loyalty program.

In accordance with an exemplary embodiment of the present invention,another tool available to the merchant on the account manger module 3310includes an offer rank module 3330. As shown in FIG. 33, the merchant3201 may view where the merchant's offer ranks among other active offersin the same category in terms of redemption and effectiveness. Forinstance, FIG. 36 shows an exemplary view of an offer rank. As shown,the offer rank for the offer from merchant 3201 is ranked in popularitywith other offers from merchants in a similar category ofproducts/services. In this example, the offer from merchant 3201 is 6thin popularity when compared with other offers from competitors.

In this example, the offer rank module 3330 provides a pull-down menu toselect a product/service category as well as the time periods forcomparison. Other types of parameters may be used for comparison withoutdeparting from the scope of the invention. While the exemplaryembodiment of FIG. 36 displays the names of competitors, the names ofmerchants other than the viewing merchant may be removed to provide ananonymous offer rank. In this manner, the merchant 3201 may be able toassess what types of offers are popular among the users as well as theeffect of the merchant's own offer in the marketplace. Based on thisinformation, the merchant may create more effective offers to get betterresults.

Offer Bid Module

In conjunction with the offer rank module 3330, the account managermodule 3310 includes an offer bid module 3340. In particular, themerchant 3201 may change its offer based on the information obtainedfrom the offer rank module 3330 and/or other marketing information(e.g., click volume data) to increase the effectiveness of the offer.(FIG. 33: Step 6) The offer bid module 3340 may be accessed from theaccount manager module 3310 or from the offer rank module 3330 (e.g.,via buttons 3610) as shown in FIG. 36. More specifically, using theoffer rank information from FIG. 36, the merchant 3201 may want toincrease the offer level (e.g., higher discount) to make the offer moredesirable. If it appears from the click volume data from the accountmanager module 3310 that there is little click traffic, then themerchant 3201 may want to increase or change the offer parameters tocreate more traffic.

FIG. 37 illustrates an exemplary view of the offer bid module 3340. Asshown, the offer bid module 3340 allows the merchant 3201 to change theoffer to be more attractive to potential consumers. For example, theoffer bid module 3340 shows a portion of the offer rank to compare thecurrent offer to those that are more successful from competitors. Theoffer bid module 3340 allows the merchant 3201 to change the level ofdiscount, for example, give free shipping, an additional gift, and/orchange the offer type (e.g., from “limited time” to “ongoing”). Othertypes of offers/incentives may be used without departing from the scopeof the present invention.

Once the offer has been changed through the offer bid module 3340, thenew offer is updated in the offer database 3335. (Step 7) The new offeris then updated to be matched and distributed to the relevant users.(Step 8) The offer bid module 3340 allows the merchants to benefit bybeing able to change the offer parameters to create a more effectiveoffer and users benefit by receiving better offers due to thecompetitive marketplace created by the offer rank module 3330.

Merchant Mapping

Merchant mapping allows for the exponential collection of preferencedata. As discussed above, reminder data is captured from a customer. Theactive data gathering module (ADG) may dynamically present a user with apreference question (e.g., a reminder) or with an offer and monitor ifthe user responds. This reminder data is collected and analyzed. Theanalysis may include filtering merchants based on the recency,frequency, and magnitude of the reminder data. The recency of the datais a rating of how old the data is. The frequency of the data is howoften a particular offer is requested. The magnitude of the data is howmany times an offer has been requested or the dollar amount of an offer.The reminder data is then used to send similar types of offers tocustomers in the future. For example, the similar types of offers mayinclude offers from the same vendor or related vendors. FIG. 38 is anexample of a merchant mapping. The example shows that customers thatrespond to offers from a retail store, such as Target®, or shop at thestore may also be interested in offers from various other merchantsdisplayed in the mapping. The closer in proximity a merchant is to thecenter of the circle, the more likely a customer is to respond to anoffer from that merchant. For example, as shown in FIG. 38, customersshopping at Target® may respond to offers from Kmark® orSmartBargains.com more than offers from Buy.com. Merchant mapping allowsfor the selling of a marketing campaign that applies to one merchant tobe sold or used by another merchant that is found in the merchantmapping.

Example

The following describes an exemplary workflow of a merchant interactingwith the merchant network services module in accordance with the presentinvention. As shown in FIG. 39, a merchant accessing the system of thepresent invention for the first time is guided through a series ofscreens, such as an auto-enroll wizard the examples of which are shownin FIGS. 40A-40D, to setup an account to begin offering a product orservice.

In particular, in step 3902, the merchant is first guided to a screenthat allows the merchant to pick a category that best describes themerchant. FIG. 40A shows an exemplary embodiment of a category interfacethrough which the merchant selects a category. In step 3904, themerchant is then guided through a screen to create anadvertisement/information about the merchant to be displayed on thesystem of the present invention. FIG. 40B shows an exemplary embodimentof an ad creation interface. As shown, the ad creation interface may bea pre-designed template with various fields that can be customized bythe merchant to create an advertisement about the merchant. In step3906, the merchant is guided through a screen to enter the contactinformation needed to send the activation information as well as tosetup a password for accessing the merchant services module of thepresent invention. (FIG. 40C) In step 3908, the merchant is notifiedthat the initial enrollment process is complete and to expect an emailmessage to confirm enrollment (FIG. 40D).

Once the initial enrollment has been completed, the merchant waits forapproval. When the approval process is completed, an email messagecontaining a link to the account manager module 3310 (FIG. 33) andinstructions on how to login is sent to the merchant. (Step 3910) FIG.41 shows an exemplary embodiment of the email message that is sent tothe merchant to confirm enrollment. When the merchant activates the link(e.g., a URL to the account manager module 3310), the merchant is guidedthrough a screen for logging into the merchant services module. FIG. 42shows an exemplary embodiment of the login interface. During the loginprocedure, the system of the account manager module 3310 determines ifthe merchant is logging in for the first time. (Step 3914) If this isthe first time logging in, the merchant is guided through a series ofscreens for setting up the account and creating an offer.

In step 3916, the merchant is guided through a screen for setup up ofthe merchant's account. FIG. 43A shows an exemplary embodiment of theaccount setup interface. In step 3918, the merchant is guided throughthe heat map screen so that the merchant may get a sense of users'activities on the system. FIG. 43B shows an exemplary embodiment of aheat map showing users' activities based on merchant categories, forexample. In the exemplary heat map of FIG. 43B, the “hottest” categoryappears to be in the “electronics” category, followed by “cellphone/wireless” and “apparel.” As described above, the merchant may also“drill down” into each of the categories (e.g., into sub-categories) toobtain a display with higher granularity. In step 3920, the merchant isguided through the offer rank screen so that the merchant may get asense of the most popular offers in a particular category of merchants.FIG. 43C shows an exemplary embodiment of an offer rank showing theoffers ranked in popularity (i.e., traffic) for a particular category ofmerchants. The merchant's offer will not be shown when the merchant islogging in for the first time as no offer has been created. The othermerchants' identities are kept anonymous to ensure privacy. In step3922, the merchant is guided through the offer tool screen so that themerchant may create an offer based on the information obtained from theheat map and the offer rank. FIG. 43D shows an exemplary embodiment ofan offer tool interface. The merchant can designate, in part, the offer,length of the offer, and any descriptions of the offer. Once the offerhas been created, a confirmation screen is displayed informing themerchant that the created offer will be made available to the users.(Step 3924).

Once the merchant has set up the account and created an offer for thefirst time, the merchant is taken to the account manager modulehomepage. All subsequent logins occur at step 3912, bypassing theauto-enroll wizard (i.e., steps 3902-3910). The login at step 3912, onceregistered, directs the merchant to a homepage 3310 a on the accountmanager module 3310. FIG. 44A illustrates an exemplary embodiment of thehomepage 3310 a. In the example shown, FIG. 44A displays an accountsummary, online lead generation information, and account activity.However, other information may be displayed without departing from thescope of the invention.

As shown in FIG. 39, the account manager module 3310 includes access tovarious management tools (3310 a-3310 e). Access to these tools isdepicted as tabs 4410 in FIG. 44A. However, other interfaces, such asbuttons, for example, may be used without departing from the scope ofthe present invention. In the example shown in FIG. 44A, the tabs 4410may include access to “Home” (3310 a), “Create Offer” (3310 b),“Increase Marketing” (3310 c), “Reporting” (3310 d), “My Account” (3310e), and “Tutorial/FAQ” (3310 f) tools. FIGS. 44A-44E show exemplarydisplays and interfaces of some of these tools.

In particular, as shown in FIG. 39, various marketing tools may beaccessed through the Increase Marketing tool 3310 c. As shown, theIncrease Marketing tool gives the merchant access to heat map module3320, offer rank module 3330, and the offer tool module 3340 withfunctionalities as explained above. Furthermore, the merchant is alsogiven the option of selecting between a “Revenue Sharing” 3950 and“Fixed Marketing” 3960 tool. Additionally, the selection of the CreateOffer 3310 b tool guides the merchant through steps 3902-3910 to createanother offer.

Payment/Registered Card Module

An exemplary embodiment of the system of the present invention includesthe following components and entities:

Users: Users are potential customers of goods and services. The usersmay be members of a loyalty program through which the incentives areoffered.

Card Issuers (partners): The card issuers are entities that offer creditor debit cards to the users. Examples of card issuers may be AmericanExpress, Bank of America, Chase, CitiBank, etc.

Processors: The processors are entities that process transactions fromcredit or debit card purchases. The processors may be separate entitiesfrom the card issuers.

Registered Cards: Registered cards are credit or debit cards issued tothe users by the card issuers that have been registered with the user'sloyalty program to be used as the main transaction vehicle for purchasesof goods and services. Examples of cards that are registered include,but are not limited to, MasterCard®, Visa®, American Express®,Discover®, Diner's Club®, and the like.

Merchants: Merchants are entities who offer goods and services to users.The merchants may offer incentives to the users through the loyaltyprogram to which the users' may be members. The incentives may rangefrom discounts to free offers as well as other perks intended to enticethe users to purchase merchants' goods and/or services.

Sponsors (User Networks): Sponsors are entities that provide loyalty orperks programs to the users. Sponsors may be employers, institutions(e.g., alumni or bar associations), and companies. Sponsors may also bemerchants or card issuers as well.

Registered Card (“RC”) Processing System: The RC processing system is amiddle system that is the backbone of the present invention. The RCprocessing system provides the registration of the cards, processestransaction data from the card issuers, matches users' transactions withthe incentives/discounts offered from the merchants, and distributesawards to users.

Registered Card Processing System

FIG. 45 shows an exemplary embodiment of a payment processing system inaccordance with the present invention. As shown in FIG. 45, the paymentprocessing system of the present invention includes a Registered Card(“RC”) Processing System 4510. The RC processing system includes a datacapture module 4510 a, rules management module 4510 b, and instructionmodule 4510 c. The data capture module 4510 a captures, among otherthings, the enrollment information such as the user information,registered card information, and loyalty program to which the user isenrolled. The rules management module 4510 b includes access to therules management database (e.g., FIG. 2: 240 c) that stores the businessrules to be applied for each user in determining the incentive to beapplied. Based on the business rules, the rules management module 4510b, among other things, calculates the discount/incentive due to theuser. The instruction module 4510 c sends instructions to the issuer orother third party processing entity of the registered card for properprocessing and applies the discount/incentive due to the user. Thediscounts/incentives include accumulating reward points (i.e., earningthe points), redeeming the accumulated points (i.e., spending thepoints), depositing the points in an account (i.e., saving the points),or giving the points to a charitable account. Some or all of thecomponents of the RC processing system 4510 may be implemented inconjunction with or independent of the narrowcasting system 38 describedabove.

The RC processing system 4510 also includes a points module 4520 and aspend, save, and give module 4530 to be described in detail below. Ingeneral, the points module 4520 maintains an accounting of accumulatedand redeemed points based on the users' activities. The spend, save, andgive module 4530 processes the various accounts to which the user hasdesignated the redemption of the points to flow.

The RC system of the present invention may be implemented on a computernetwork using Internet or Intranet portals. The users may be givenaccess to the portal that is specific to the users' enrolled loyaltyprogram. The RC system of the present invention may be accessed by theuser at any end-user client device, such as computers, kiosks, andmobile devices that is connected to the system via a local area network(LAN), wide area network (WAN), Internet, peer-to-peer connections(i.e., direct connections via modem, for example), or wireless networks.The portals may be implemented on the system of the present invention ormay be implemented on separate systems.

RC System Workflow

A loyalty or reward program for a sponsor, such as an employer who wantsto provide a benefits program to provide incentives for its employees,may created in conjunction with or independent of the narrowcastingsystem 38 as described above. A loyalty program portal, for example, maybe implemented on the system of the present invention or may beimplemented by a separate system. The sponsors may be any entity,including merchants and card issuers, who want to provide benefits andincentives to its members in exchange for their loyalty to the sponsor.

Once a loyalty program is set up, the sponsor notifies its intendedusers regarding the loyalty/perks/rewards program and encourages theusers to enroll in the loyalty program. The loyalty program providesincentives to the users by making available offers from merchants thatwould peak the users' interests. The matching of the incentives fromvarious merchants to the most appropriate users is explained above.

To enroll, the user accesses the portal and provides the necessaryinformation to become a member of the loyalty program. Duringenrollment, the user is provided the opportunity to register a paymentcard to be used in purchase transactions resulting from the incentivesprovided by the loyalty program. The user information and the paymentcard information are captured by the data capture module 4510 a. Inparticular, the users' personal information and the card informationassociated with the user are stored in a user database. In addition, theportal, as shown in FIGS. 56A-56F, allows a user to access and viewdiscount/incentives that is available to the user or the user hasreceived, including any reward points earned or redeemed by the user.

FIG. 46 shows an exemplary embodiment of the RC processing system 4510in accordance with the present invention. FIG. 47 shows a workflowdiagram of an exemplary process according to the present invention. FIG.55 shows an example of the overall registered card purchase transactionflow. As shown in FIG. 46, the RC processing system 4510 is an interfacebetween the rewards/loyalty program, the card issuer, the processor, andthe merchant. In particular, a user accesses the rewards/loyalty programthrough a portal as shown in FIGS. 56A-56F, for example. Therewards/loyalty portal includes an enrollment module 4610 through whichthe user may register as a member to the rewards/loyalty program. Duringenrollment, the user is asked to register a payment card to be used forperforming transactions to take advantage of the merchant incentivesoffered through the rewards/loyalty program (FIG. 47: step 4701). Whilethe card may be registered during enrollment, the user may register acard, add additional cards, or change a registered card with another, atany time through the rewards/loyalty program portal without departingfrom the scope of the invention.

The enrollment information and/or the card information, including thecardholder name and card number, are captured in the data capture module4510 a. The card may be registered by passing the card informationdirectly to the data capture module 4510 a or through a surrogate. Thatis to say, instead of passing the card information directly to the datacapture 4510 a, in an alternate embodiment, the enrollment module 4610may contact the card-issuer to receive a surrogate ID to be used inplace of the actual card information. Once the data capture module 4510a receives the user information including the card information (eitherthe actual card information or a surrogate ID), the data capture module4510 a sends to the transaction reporting module 4620 the registeredcard information (FIG. 47: step 4702). Optionally, a list ofparticipating merchants may also be sent (FIG. 47: step 4702). Thetransaction reporting module 4620 updates the cardholder data files andparticipating merchant list (FIG. 47: step 4703). In this way, the cardissuer or processor monitors any transactions occurring at theparticipating merchant associated with the registered card.

Acceptance of Offer

Once the user has enrolled in the loyalty program (now a “member”), theuser is provided with a list of incentives and offers from merchantsthat would most interest the user on the loyalty/rewards program portal.In an exemplary embodiment, the incentives and offers may benarrowcasted to the user through the narrowcasting system 38 asdescribed above. However, the narrowcasted incentives/offers are notrequired. The incentives offered to the user are stored in the rulesmanagement module 4510 b. In one exemplary embodiment, the user can takeadvantage of the incentive/offer made available to the user on theportal by simply going to the particular vendor related to the incentiveand making a purchase. The purchase may be made at the physical store,on-line, over the phone, through the mail, or any other purchase channelas long as the user uses the registered card.

In another exemplary embodiment, the user can sign up (i.e., reserve) totake advantage of the incentive or offer through the loyalty/rewardsprogram portal. This is a form of “RSVPing” (i.e., reserving) the offeror incentive for use in the future. By signing up for the offer orincentive, the payment processing system of the present invention canaccumulate analytics of the user's purchasing behavior. These analyticsmay be input into the narrowcasting system 38 as additional data sets tofurther enhance the relevance of future offers to be made to the user aswell as for market reporting features for the merchant who made theoffer. Accordingly, this tool is useful for proving incrementality of anoffer. This tool is also useful in limiting the redemption of anincentive to a present number of people or consumers (i.e., “offercontrol).

Partially Qualified Transactions (PQTs)

During a purchase from the merchant offering an incentive, the user usesthe payment card registered with the RC processing system 4510. Asbriefly discussed above, the purchase may be made on-line (i.e., throughthe merchant's website), in person at a physical location of themerchant, through a mail order catalog, by phone, or any other methodwithout departing from the scope of the invention (FIG. 47: step 4704).The transaction reporting module 4620 monitors the registered cardactivities and identifies transactions made with the participatingmerchant using a registered card as a partially qualified transaction(“PQT”) (FIG. 47: step 4705). Identified PQTs are then sent to the rulesmanagement module 4510 b of the RC processing system 4510 (FIG. 47: step4706). In an exemplary embodiment, the RC processing system 4510 maytrack both an offer that is reserved as discussed above and an offerthat has been used by a customer.

In the rules management module 4510 b, a matching engine 4624 matchesthe PQTs with the associated merchants and passes the information to arewards calculation module 4626 to determine the type and amount of theincentives/reward, if any, based on stored business rules (FIG. 47: step4709). In this regard, the matching engine 4624 may analyze the PQT tomatch the transaction based on merchants and/or products. Product basedmatching will be further explained in detail below. Moreover, therewards calculation module 4626 applies stored business rules from abusiness rules database (e.g., FIG. 2: 240 c) to the received PQTs.

The business rules may be defined by the merchants to specify the termsof the offer to be made to the user. The rules management module 4510 bprocesses the PQT based on rules associated with the user. The businessrules database (e.g., FIG. 2: 240 c) connected to the rules managementmodule 4510 b contains all of the business rules associated with all theincentives made available to the users. FIG. 49 describes examples ofthe different types of rules that are available through the registeredcard system.

For example, the rules may include time-based criteria (e.g., timeperiod in which the incentives offered are valid), user-specificcriteria (e.g., incentive only available to members of a specificloyalty program), and terms of the incentive/discount. Other businessrules related to the incentive are stored in the rules database to beapplied in processing the transaction data For example, the conditionsmay include, but are not limited to, the type of incentive, the amountof incentive, to whom the incentive applies, the time frame for theoffer, and any other conditions of the offer. Moreover, the samemerchant may target a specific type of user by customizing the amount ofthe incentive or terms of the offer down to the individual level. Inorder words, an offer by the same merchant may be different betweenusers based on the users' profiles. In this way, the merchants cancustomize the offer as generally or as detailed as the merchant desires.The rules management module 4510 b automatically applies the rules tothe user's PQT to determine the level of discount/incentive based onthese rules.

Offer Types

The following types of incentives or offer types may be made bymerchants. These offer types are associated with the rules specified bythe merchants. A first offer type is the delivery of discounts orsavings, such as a percentage or dollar amount off, a percentage ordollar amount off of a purchase greater than a set dollar amount, or apercentage or dollar amount off up to a certain maximum discount. Asecond offer type may be used to attract new customers or bring backloyal customers. The offer may include allowing each user to utilize theoffer one time for a purchase. For example, this offer type may state“50% off Next Purchase” or “$100 Savings on First purchase.” A thirdoffer type may include making offer “A” available the first time acustomer makes a purchase and making offer “B” available for allsubsequent purchases. A fourth offer type may include allowing forrepeating charges to be discounted for a set period of time. An examplemay include “25% off your first six months of delivery.” A fifth offertype may include tiered offers or offers based on dollar ranges ofmerchandise. An example may include “10% off purchases up to $99, 20%off purchases $100-$999, and 30% off purchases $1000+.” A sixth offertype may include an offer that is available a certain number of timesper period of time.

In addition, a seventh offer type may include an offer that will beavailable on a specific day of the week. An eighth offer type mayinclude requiring a user to view the Offer Detail Page within aspecified window of time or to view and take active steps (i.e.,clicking on a web page) to take advantage of the offer. This type ofoffer eliminates accidental discounts for a customer who would have paidfull price. This offer type is discussed above with regards to reservingan offer. A ninth offer type may include imposing minimum and/or maximumspending and discounts. A tenth offer type may include an offer that isindividualized for a particular user. For example, this user-level offermay include giving person A 10% off and person B 20% off. An eleventhoffer type may include network level offers that allow a merchant tomake an offer available to an entire network (or segments of thenetwork). For example, company A employees get 10% off and company Bemployees get 20% off. These offer types can be combined to createcustomized solutions and all offer types can be based on dollar orpercentage calculations. In addition, an offer type can be used tocreate cross promotion of products.

By way of example, a flower vendor may create an offer for free shippingto all men who live in New York City that purchase 5 dozen roses onFebruary 13 between the hours of 8:00 am and 10:00 am using a specifictype of credit card. Another example is a merchant creating an offerthat only a small number of consumers can take advantage of. Once theoffer has been used by a set number of consumers, a merchant can createa second offer. The second offer is typically a lesser offer, but stillprovides consumers with an incentive to make a purchase. Any businessrule for the offer may be made without departing from the scope of thepresent invention. In particular, criteria for determining the businessrule to maximize the merchant's marketability may be performed by thenarrowcasting system of the present invention as described above. Othertypes of offers and any combinations thereof may be used withoutdeparting from the scope of the invention.

Fully Qualified Transactions (FQTs)

Once the PQTs have been processed by the matching engine 4624 andrewards calculation module 4626 to verify and determine the type andamount of the incentive, if any, the PQTs are converted to fullyqualified transactions (“FQT”s). The FQTs and the determined rewardsassociated thereto are sent to the instruction module 4510 c. Theinstruction module 4510 c sends instructions to the card issuer tocredit back the user based on the calculated discount, for example,offered by the merchant for this user (FIG. 47: step 4709). For example,the instruction module 4510 c sends credit data to a user creditingmodule 4630, which applies the credit data to a user statement module4640 to reflect the credit given to the user on the user's monthlystatements. The instruction module 4510 c also updates the pendingcredit due to user in the user's account accessible through therewards/loyalty program portal (FIG. 47: step 4708). The card issuerthen credits the user's account in the amount identified by theinstruction module 210 c (FIG. 47: step 4710) and the credit amount isreflected in the user's monthly card statement (FIG. 47: step 4711).

The card issuer receives the discount information from the instructionmodule 4510 c and directly applies the credit to the user's registeredcard account. The original transaction amount and the credited discountamount are reflected as separate transactions in the user's cardstatement. FIG. 48 shows an exemplary statement generated in accordancewith the present invention. Accordingly, the user is only obligated torepay the card issuer at the discounted price. In this regard, the cardissuer has several options for processing the discount. The card issuermay withhold the amount of the discount before paying the merchant,especially if the transactions are batched over a period of time (e.g.,monthly basis). If the merchant has already been paid at the regularprice of the goods or services, the card issuer obtains the amount ofthe discount from the merchant directly. In an alternative embodiment,the RC processing system 4510 of the present invention may act as anintermediary, thereby paying the credit amount to the card issuer andrecovering the same amount from the merchant by billing the merchant.Other payment options between the card issuer and the merchant may bemade without departing from the scope of the present invention.

Merchant Matching

In order to create PQTs and FQTs, the card transaction data must bematched with the merchants to determine the incentives/discounts, ifany. FIGS. 50 and 51 illustrate exemplary embodiments for processing thecard transaction data to match the transactions to the proper merchants.Merchant matching allows for the identification of merchant transactionsfrom data feeds provided by the card issuer or a third party processor.The merchant matching software (i.e., matching engine 4624) allows forthe inclusion of any merchant that is registered with the registeredcard system. The merchant matching process also solves an issue ofidentifying merchants from datasets that do not include unique merchantidentifications as is typical with transaction data from issuers orprocessors.

The process of merchant matching includes the following steps as shownin FIGS. 50 and 51:

-   -   1. A user makes a purchase at a merchant 5110 and the        transaction data, such as card member information and merchant        information, and identifying information for the item or items,        is forwarded to a processor 5120.    -   2. The merchant transaction data from the processor is forwarded        to the registered card system 4510.    -   3. A card issuer 5130 receives the card member transaction data        from the processor 5120.    -   4. The card issuer 5130 sends an identified PQT to the rules        management module 4510 b of the registered card system 4510 as        discussed above. The PQT may include the card member information        and the purchase made.    -   5. The registered card system 4510 normalizes the transaction        data received from the processor 5120. The data normalization        process may be any type of appropriate normalization process        known in the art.    -   6. The registered card system 4510 identifies the merchant data        in the transaction data. The registered card system 4510        verifies that the merchant is registered with the registered        card system 4510. This step may include determining whether the        merchant's name or other merchant identifying information, such        as the merchant location, store identification, or industry        code, in the transaction data matches or correlates with a        merchant name or other merchant identifying information that is        stored within the registered card system. This step may also        include verifying that the merchant is actually offering a        particular incentive that is found in the transaction data.    -   7. The matching engine 4624 of the registered card processing        system 4510 matches the PQT with the associated normalized        merchant data and passes the information to a rewards        calculation module 4626 to determine the type and amount of the        reward, if any, based on stored business rules (FIG. 47: step        4709).    -   8. Finally, if the PQT is a qualified transaction, then a FQT is        transmitted from the registered card system 4510 to the card        issuer 5130. The remaining steps are similar to those discussed        above with regards to the processing of FQTs.

In accordance with the exemplary embodiment of the payment processingdescribed above, the user does not have to clip coupons, remember couponcodes, or perform any other extraneous activities to take advantage ofan offered incentive. Once the user registers a card with therewards/loyalty program, all the user has to do is use the registeredcard to make purchases at participating merchants. Because the cardissuer authorizes the transaction at the regular price of the offeredgoods or services at the time of purchase, the sales/servicerepresentatives do not have any indication that the user is obtaining adiscount. From the merchant's perspective, the user is a regularcustomer making a regular purchase. Moreover, because the incentives areautomatically processed after the purchase, the users are notifiedimmediately of the pending discounts or rewards. Finally, any discountsare automatically applied to the card account before issuing the cardstatement, thereby receiving the benefits of any savings directly. Someof the benefits of the present invention are listed below:

From the customers' perspective, the registered card automated incentiveredemption system provides:

Faster redemption of incentives: Incentives are processed automaticallyby the card issuer and the incentives are applied directly to the cardtransaction.

Easier redemption: There are no coupons or certificates to clip, print,carry, and produce at the time of purchase.

Better purchase experience: A customer cannot forget to take the couponor certificate, or suffer the embarrassment of producing the coupon orcertificate at the register.

From the merchants' perspective, the registered card automated incentiveredemption system provides:

Cheaper promotions: Eliminates administrative cost to produce andprocess paper coupons or promotions.

Easier promotions: Less administrative cost for processing redemptionswith the paperless transactions and efficient sales processing. Thesystem also provides better tracking of promotions and effectiveness.

Secure promotions: No coupon leakage or viral distributions of promotioncodes. The system allows for private sales that are discreet andexclusive with complete control of intended customers.

Tracking of purchases: Merchants are able to track the purchases of allcustomers using a coupon (i.e., purchase funnel).

From the card issuer and the rewards/loyalty sponsor's perspective, theregistered card automated incentive redemption system provides:

Concentrated purchases: All purchases are on one card allowing increasedusage of the card.

Irreplaceable transaction mechanism: Combining huge discounts on largerpurchases with good discounts on everyday spending makes the cardinvaluable to the user (i.e., the user will always use the card in theoff chance that the user will get a discount).

Super-charges the rewards program: The rewards/loyalty program gets aboost of usage as users discover the convenience and benefits of usingthe card.

SKU Level Discount Processing

As briefly described above, another exemplary embodiment of the presentinvention includes a SKU (i.e., stock keeping unit) discount processing.Currently, many grocery chains, as well as other merchandise-basedvendors offer membership or club cards to customers to provide discounts(e.g., “membership price”) and rewards. These loyalty programs areintended to offer discount prices to members while keeping track of thetypes of goods purchased by the customers.

The RC processing system 4510 of the present invention receives SKUlevel discount information from merchants and manufacturers and storesthe information in the rules database (not shown). The user, who hasregistered a transaction card with a loyalty program of a merchant(e.g., grocery store), makes purchases at the merchant's store (eitheron-line or in the store) using the registered card. As described abovein reference to FIGS. 46 and 47, the transaction reporting module 4620of the card issuer monitors the transaction activity of the registeredcard and identifies PQTs (i.e., transactions on the registered cardassociated with the merchants participating on the loyalty program). Theidentified PQTs are communicated to the rules management module 4510 bof the RC processing system 4510. In addition, the merchant sends SKUlevel purchase data to the rules management system 4510 b of the RCprocessing system 4510. Similar to identifying the merchants associatedwith the PQTs described above, the matching engine 4624 matches the PQTswith the SKU level purchase data from the merchant to identify thespecific products associated with the PQTs purchased with a particularmerchant. In this regard, the business rule database (not shown) hasstored therein SKU level purchase data received either from the merchantor, more typically, from a third party marketing institution thatgathers and processes SKU level purchase data for the merchant. Thematching engine 4624 applies the SKU level discount rules to the SKUlevel transaction data to identify, validate, and verify the PQTs madeby the user and converts them into FQTs. The rewards calculation module4626 applies the stored business rules, such as the discounted purchasescorresponding to the card transaction data (e.g., by date, location,transaction amount, etc.). The rewards calculation module 4626 thencalculates the amount of discount or points for each qualified SKU itemand determines the total amount of discount or points due to the user.

The total discount amount to be credited to the user or the amount ofpoints earned by the user for the purchase of an item is sent to thecard issuer through the instruction module 4610 c. The card issuer thencredits the discount amount or points amount to the user's registeredcard account, for example, and generates a monthly card statement. Themonthly statement may list the SKU information with the associateddiscounts or points to provide a record of the items to which thediscounts or points were applied and the amount of savings associatedwith each item. As the discount has already been applied before issuingthe statement, the user is only responsible for repaying the card issuerthe discounted transaction amount. As discussed before, the card issuermay obtain the discounted amount from the merchant directly or the RCprocessing system 4510 may act as an intermediary. In this manner, theuser does not have to clip any coupons or keep track of a separateclub/rewards card in order to take advantage of the loyalty basedsavings. Moreover, because the card issuer has processed the FQTsincluding the SKU level discounts, the processed information may be usedby the merchant to recover any reimbursements from manufacturer baseddiscounts, thereby simplifying the incentives offered by themanufactures while reducing fraudulent coupon redemptions by themerchants from the manufacturers.

Reward Points Module

In an exemplary embodiment of the present invention, a points module4520 adds enhanced features to the RC processing module 4510. The pointsmodule 4520 may be implemented independently or in conjunction with theRC processing system 4510 without departing from the scope of thepresent invention. In general, the points module 4520 allows users toaccumulate points based on specified activities and redeem theaccumulated points for various rewards, such as to purchase goods orservices or apply the points to spend and save accounts described infurther detail below.

Earning Points

Generally, an enrolled member accumulates reward points based on rewardrules set by the sponsors and/or merchants (e.g., number of visits,qualified purchases, performance award, etc.). The accumulated pointsare maintained and tracked through the user's account on theloyalty/rewards program portal as shown in FIGS. 56A-56F, for example.In the exemplary embodiment of the present invention, the points module4520 defines a specified amount of points (e.g., 1 point) to represent aspecified monetary amount (e.g., $0.01). The points module 4520allocates a specified number of points for a specified type of activitybased on reward rules. As discussed above, these reward rules may bedefined by the sponsors and/or merchants. For instance, the merchantsmay add points that correspond to the amount of discount for variousproducts and services rather than giving monetary discounts (e.g., 100points for every $1 spent, 10 points/$ for 10% discounts, etc.).

FIG. 52 shows an exemplary process for accumulating points. As shown inFIG. 52, a user (“Bob”) visits a participating merchant and makes a $100purchase using his registered card. As explained above with reference toFIGS. 46 and 47, the transaction is authorized and settled by the cardissuer as a normal credit card transaction. During the card transactionprocessing, the card issuer realizes that the transaction is aregistered card transaction with a participating merchant. Thetransaction is flagged as a PQT (partially qualified transaction) andsent to the RC processing system 4510. The RC processing system 4510performs the merchant matching then applies the business rules andreward rules to determine incentives/discounts that Bob is entitled to.In this example, the business rules also indicate that the merchant isoffering points for spending a set amount with the merchant. In thisexample, 1 point is to be allocated for every $1 spent on a qualifiedtransaction. Because Bob spent $100 with the merchant, the RC processingsystem 4510 adds 100 points to Bob's account loyalty/reward account.While the exemplary embodiment of FIG. 52 is described with the standardpoint accumulation process, additional points may be allocated to theuser if the sponsor of the rewards/loyalty program has reward rules setto provide an incentive to the user to use the program. Additionalpoints may be added by reward rules set by the merchants to furtherencourage usage. The reward rules for adding points may be customizedfor each user without departing from the scope of the present invention.

Burning Points

Once the points have been earned, the points module 4520 maintains apoints balance for each user. The user accesses the points balancethrough the rewards/loyalty program portal, for example. In accordancewith the present invention, the accumulated points may be redeemed invarious ways. For instance, the points may be redeemed for cash, appliedagainst a purchase, or designated into various savings vehicles throughthe spend, save, and give module 4530. Regardless of how the points areto be redeemed, the user accesses the points module 4520 through theuser's account on the rewards/loyalty program portal. Under the user'saccount accessed through the portal, the points module 4520 displays thetotal balance of the accumulated points available for redemption. Oncethe user decides to redeem the points, the user designates the number ofpoints to be redeemed and where the points are to be applied (e.g.,cash, merchant, spend and save, etc.)

Spending Points

If the user decides to cash in the points, the user designates theamount of points to redeem and selects the “Cash” option. The pointsmodule 4520 sends the request to the rules management module 4510 b. Therules management module 4510 b applies the stored conversion rate (e.g.,1 point=1¢) to determine the monetary value of the points. Once theamount of the reward is calculated, the instruction module 4510 c sendsan instruction to issue payment to the user.

If the user decides to spend the points at a merchant, the userdesignates the amount of points to redeem and selects the merchant wherethe points will be used. More than one merchant may be designatedwithout departing from the scope of the invention. Thereafter, the usercan visit the merchant (e.g., on-line, in store, by phone, throughcatalog, by mail, etc.) to make a purchase. When making the purchase,the user uses the registered card and the transaction occurs like anyother purchase at the regular price. As discussed above, the card issuerthen identifies the transaction as a PQT (i.e., a registered cardtransaction at a participating merchant). The PQT is sent to the RCprocessing system 4510 to be processed as described above to determineany discounts and/or incentives are to be applied. During thetransaction/product matching stage the rules management module 4510 brecognizes that points are designated to the identified merchant.Accordingly, the predesignated amount of points is converted to amonetary equivalent and the value is deducted from the transactionprice. If the rules management module 4510 b identifies additionaldiscounts/incentives offered by the merchant, those discounts/incentivesare also applied. Once all of the discounts, incentives, and rewardshave been applied, the PQT is converted to an FQT and passed to theinstruction module 4510 c. As explained above, the instruction module4510 c then issues the FQT to the card issuer to instruct the amount ofcredit to be applied to the user's account. If the purchase priceexceeds the amount of points redeemed in addition to any otherincentives/discounts, then the difference is charged against theregistered card account. If the purchase price is less than the amountof points redeemed, then no charge is made against the registered cardaccount and any points left over are kept as designated for redemptionat the specified merchant. Each transaction is then reflected in theuser's registered card statement. In addition, the pending redemption ofthe points and any pending discounts/savings are calculated by the rulesmanagement module 4510 b and displayed under the user's account in therewards/loyalty program portal. After the FQT has been processed and theuser notices any left over points, the user may de-designate the pointsfor redemption (i.e., put the points back into the total points balance)or use the left over at the merchant at another time.

FIGS. 53 and 54 illustrate an example of the points redemption processthat includes a discount offer from the designated merchant. As shown inFIG. 53, a user (“Bob”) accesses his account through the rewards/loyaltyprogram portal and sees a total points balance of 85,000. Bob browsesthrough the portal to see various offers made to his rewards/loyaltyprogram members and notices that one of the merchants (“Star Trac”) isoffering a 25% discount on a $1000 item. Bob decides he wants to pay forthe item with his points. Bob designates 75,000 points (i.e., $750 inthis example) to redeem and selects the merchant (“Star Trac”) where hewill redeem the points. After designating the points to be redeemed, Bobgoes to the merchant (on-line, in store, by phone, or by mail order) anduses his registered card to make the purchase. The card issuerauthorizes the transaction for the full price of the item (i.e., $1000).The card issuer recognizes the transaction as being a PQT (i.e., aregistered card transaction at a participating merchant) and forwardsthe PQT to the RC processing system 4510. The PQT is matched to thecorresponding merchant (“Star Trac”) and the business rules for themerchant are applied to the PQT. At this time, the RC processing system4510 recognizes that Bob had designated 75,000 points to be applied totransactions from this merchant. The RC processing system 4510 alsoidentifies that the merchant is offering a 25% discount (i.e., $250) tomembers of the rewards/loyalty program. Accordingly, the PQT (i.e.,$1000) is reduced by the discount (i.e., $1000−$250=$750). In reality,the RC processing system 4510 charges the merchant for the discountamount (i.e., $250). The remaining balance (i.e., $750) is paid by theRC processing system 4510 from the redeemed reward points. From anaccounting perspective, because the card issuer, in effect, has paid themerchant the full purchase price (i.e., $1000), the FQT generated by theRC processing system 4510 authorizes the card issuer to debit theaccount maintained by the RC processing system 4510, thereby bringingthe user's registered card account to $0. Accordingly, the RC processingsystem 4510 updates the user's account in the rewards/loyalty programportal with the discount/redemption information to reflect a new pointsbalance (i.e., 10,000). The card issuer, likewise, reflects in theregistered card user's statement the initial purchase ($1000), theapplied discount from the merchant ($250), and the amount from theredeemed points ($750), each as a separate line item on the statement.

FIG. 54 illustrates another example of the points redemption process. Inthis example, the points are redeemed at two different merchants with nodiscounts offered by the merchants. As explained above, Bob accesses hisaccount on the rewards/loyalty program portal and notices his pointsbalance (i.e., 10,000). He decides to burn all of his points anddesignates 5,000 points at Merchant A and 5,000 points at Merchant E.Thereafter, Bob makes purchases at Merchant A (i.e., $100 purchase) andat Merchant E (i.e., $75 purchase) using his registered card. The cardissuer authorizes the charge each time for the full amount (i.e., $100and $75, respectively). The card issuer identifies these transactions asPQTs and issues two PQTs. The RC processing system 4510 matches thesePQTs and identifies the first PQT as being with Merchant A and thesecond PQT as being with Merchant E. When applying the business rules ofthese merchants, the RC processing system 4510 recognizes that there areno offers outstanding. The RC processing system 4510 also recognizesthat the user has designated 5,000 points to be redeemed at Merchant Aand 5,000 points to be redeemed at Merchant E. The RC processing system4510 converts the designated points to monetary values (i.e., $50 foreach merchant) and generates FQTs. As explained above, the card issuerhas already paid the merchants for the full amount (i.e., $100, $75).Therefore, each FQT authorizes the card issuer to debit the accountmaintained by the RC processing system 4510 by the points redemptionamount (i.e., $50+$50=$100). The remaining balance is applied to Bob'sregistered card account. Accordingly, the RC processing system 4510reflects Bob's account as having 0 point balance with any pendingtransactions as being completed (i.e., displays that 5,000 points havebeen redeemed at Merchant A and 5,000 points have been redeemed atMerchant E). Furthermore, the card issuer issues a statement reflectingthe full purchase price of the purchases (i.e., $100, $75), the amountof points redeemed and applied to the purchase price (i.e., $50, $50),and the total balance due to the card issuer (i.e., $50+$25=$75). Eachof these transactions will be reflected as separate line items on thecard statement.

Saving and Giving Points

In an alternative embodiment, the RC processing system 4510 includes aspend, save, and give module 4530. In particular, the spend, save, andgive module 4530 manages various accounts to which the points designatedin the points module 4520 can be sent. The various accounts include, butare not limited to, checking/savings accounts, investment accounts, loanrepayment accounts, and even charitable accounts. As shown in FIG. 1,the loyalty/reward system 30 of the present invention interfaces withcharity organization 60 and Asset Management system 70. The operation ofthe spend, save, and give module 4530 may operate in conjunction withthe points module 4520 or operate as a separate module. The allocationof the points operates in the same manner as the points module 4520designating points to be burned at specified merchants. That is, ratherthan merchants, the user will designate the various accounts in whichthe values of the points will be transferred. For instance, the user maydesignate that a predetermined minimum number of points, onceaccumulated, be transferred to one or more accounts designated in thespend, save, and give module 4530. Accordingly, the accumulated pointsmay be directly deposited to checking accounts, savings accounts,investment accounts (e.g., IRAs, mutual funds, education, etc.), loanrepayment accounts (e.g., mortgages, equity loans, line of credit,credit cards, etc.), and even charitable accounts (e.g., Red Cross,Goodwill, etc.). Other accounts may be designated without departing fromthe scope of the invention. Moreover, the user can access the spend,save, and give module 4530 through the rewards/loyalty program portal toview/designate/modify the savings information including disbursementsand balances. More than one account may be designated without departingfrom the scope of the invention.

As discussed above, traditional reward redemption programs require theuser to select offered items for redemption from a “rewards catalog.”These items are generally overstock or outdated items that are sold inbulk to clearinghouses that contract with the sponsors of the rewardprogram to accept the reward points as consideration for the items.Accordingly, users have extremely limited selections of items to redeemwith their points. Furthermore, because the reward points must besubjected to a claims process, the user must wait several days, if notweeks, before the item is delivered. The points module according to thepresent invention has no such limitations. The user may purchase anyitem from any merchant. Moreover, because the payment with points istransparent to the merchant (i.e., the merchant is paid outright by theregistered card), the transaction and delivery is processed andfulfilled as with any other sales. Accordingly, the loyalty programbenefits as users find more value in the reward points, and thereforeuse the loyalty program more frequently. The card issuers benefitbecause more transactions are placed on the registered card, therebygenerating more revenue while administrative processing is performed bythe RC processing system of the present invention. The users benefitbecause the points, in whole or in part, may be used with any merchantfor any item. When the points are used to purchase items with merchantincentives, the discounts are automatically processed and combined withthe points usage. All of the savings are then reflected conveniently onthe users' card statements. Moreover, the points may be used as savingsvehicles to be applied to various accounts designated by the user tofurther enhance the usefulness and convenience of the rewards/loyaltyprogram, thereby generating even more usage.

Mobile Device Messaging and Transactions

As shown in FIG. 1A, in an exemplary embodiment, users are able to usecell phones or any other mobile device 50 to search for offers and toreceive merchant offers and incentives. A user may be required toregister the mobile device 50 with the RC processing system 4510. If amobile device 50 is registered with the RC processing system 4510, thentargeted offers can be delivered to the registered users' mobile devices50. Registration includes registering a user's mobile device phonenumber and associating this number with the user's registered card orcards. The mobile device 50 must be registered to determine whether asearch for an offer is associated with a registered card holder.

After the user registers the mobile device, a user is able to search foran offer based on criteria such as location, category, brand, and typeof discount. For example, if a user is shopping in a particulargeographic area of New York City, the user could determine whether anyincentives or offers were available based on the location of the user.

To use the registered card system from a mobile device, the RCprocessing system 4510 must determine whether the mobile device phonenumber associated with a message or request for a search is being sentfrom a registered mobile device. The RC processing system 4510determines whether the phone number is associated with a particularregistered card or cards. If the phone number is associated with aregistered card or cards, the RC processing system 4510 will send anoffer or incentive to the mobile device. This offer or incentive sentcan be based on the user's search criteria. The user is then able totake advantage of the offer at the merchant associated with the offer.The registered card system and process then proceed as discussed above.

Having described the various exemplary embodiments of the presentinvention, it will be apparent to those skilled in the art that variousmodifications and variations can be made to the communication system andmethod for narrowcasting based on the active learning system and to thesystem and method for merchant network services of the present inventionwithout departing from the spirit or scope of the invention. Thus, it isintended that the present invention cover the modifications andvariations of this invention provided they come within the scope of theappended claims and their equivalents.

1. A communication system, comprising: (a) one or more client devices incommunication with at least one communication network; (b) one or moreuser data stores in communication with the communications network andbeing to store user data of one or more users using respective ones ofthe client devices; (c) one or more offer data stores in communicationwith the communications network and being to store one or more offersfrom one or more merchants; and (d) a narrowcasting engine including (i)an active data gathering module to collect the user data, and (ii) anactive learning module to generate a user profile based on the userdata, the user profile including inferred preference of the one or moreusers, wherein the narrowcasting engine is to select dynamically one ormore offers from the offer data store based on the user profile, andcommunicate the selected one or more offers in the offer data store tothe one or more users.
 2. The communication system of claim 1, whereinthe user data collected by the active data gathering module includesdemographic, behavioral, and preference data.
 3. The communicationsystem of claim 2, wherein the preference data includes a request forfuture reminders of past lost opportunities.
 4. The communication systemof claim 2, wherein the behavioral data includes at least one ofclick-throughs, hovers, and search terms of offers presented on the oneor more client devices.
 5. The communication system of claim 1, whereinthe active data gathering module includes a preference game forobtaining preference data of the one or more users.
 6. The communicationsystem of claim 1, wherein the profile generated by the active learningmodule includes a persona type, selected from a predetermined set ofpersonas, for the one or more users.
 7. The communication system ofclaim 1, wherein the profile generated by the active learning moduleincludes an indication of a life stage of the one or more users.
 8. Thecommunication system of claim 1, wherein an initial set of user data ofthe one or more users is received from a sponsor of a loyalty or rewardsprogram.
 9. A communication system, comprising: (a) one or more clientdevices in communication with at least one communication network; (b)one or more offer data stores in communication with the communicationsnetwork to store one or more offers from one or more merchants; and (c)an offer ranking module to rank the offers in the one or more offer datastores based on popularity.
 10. A communication system, comprising: (a)one or more client devices in communication with at least onecommunication network; (b) one or more offer data stores incommunication with the communications network to store one or moreoffers from one or more merchants; and (c) an offer bidding moduleuseable by the one or more merchants via the one or more client devicesto modify the merchant's offer based on a rank of the merchant's offer.11. A method for communication, comprising: (a) collecting user data ofone or more users in a user data store; (b) storing one or more merchantoffers in an offer data store; (c) generating a persona of one or moreusers based on the user data; (d) storing the persona in a persona datastore; (e) segmenting the one or more offers in the offer data storebased on the persona stored in the persona data store; (f) segmentingthe one or more users into one or more segmentation cells; (g) matchingthe one or more offer mixes with the one or more user segmentation cellsbased on rules associated with each cell; and (h) transmitting the offermix to the one or more users.
 12. The method of claim 11, wherein therules include at least one of suppression rules, designation rules, andoffer mix integration rules.
 13. The method of claim 12, wherein theoffer mix integration rules determine which offers are to be combined toform the offer mix.
 14. A system for creating a merchant offer,comprising: (a) one or more client devices in communication with acommunication network; (b) an enrollment module to solicit and receivemerchant information and an offer information; (c) a heat map module todisplay on the one or more client devices consumer activity on thecommunication network; and (d) a data store to store the merchantinformation and the offer information.
 15. The system of claim 14,wherein the display of the consumer activity generated by the heat mapmodule includes a graphical representation depicting varying levels ofactivity over a period of time based on at least one of product, producttype, and merchant.
 16. The system of claim 15, wherein the graphicalrepresentation includes at least one of varying shapes, sizes, or colorin proportion to the varying levels of activity.
 17. The system of claim14, further comprising an offer ranking module to rank the offers in thedata store based on popularity.
 18. The system of claim 17, furthercomprising an offer bidding module accessible by the one or moremerchants to modify the merchant's offer based on the rank of themerchant's offer from the offer ranking module.
 19. A system,comprising: (a) a card transaction processing module to generatepurchase transaction data associated with a payment card; (b) an offerdata store including one or more offers from one or more merchants; and(c) a transaction matching module to receive the purchase transactiondata associated with the payment card and match the purchase transactionwith the one or more merchants in the offer data store.
 20. The systemof claim 19, further comprising a rewards module to determine anincentive to be applied to the payment card based on any offerassociated with the matched merchant and to generate a qualifiedtransaction data to be transmitted to an issuer of the payment card. 21.A system, comprising: (a) an offer data store including one or moreoffers from one or more merchants; (b) a registered card module toregister one or more payment cards to be used for a purchasetransaction; (c) a transaction matching module to match purchasetransaction data resulting from the purchase transaction with the one ormore merchants in the offer data store; and (d) a rewards module todetermine an incentive to be applied to the one or more payment cardsbased on any offer associated with the matched merchant and to generatea qualified transaction data to be transmitted to an issuer of the oneor more payment cards.
 22. The system of claim 21, further comprising acard processing module to determine the amount to be credited back tothe one or more payment cards identified in the qualified transactiondata.
 23. The system of claim 22, further comprising a statementgenerator to generate a card statement including an itemized listing ofthe purchase transaction and the amount credited back to the one or morepayment cards.
 24. The system of claim 21, further comprising a pointsmodule to convert a designated number of points into a monetary valueand apply the converted monetary value to the purchase transaction. 25.The system of claim 21, further comprising a points module to convert adesignated number of points into a monetary value and apply theconverted monetary value to a saving account.
 26. The system of claim21, further comprising a points module to convert a designated number ofpoints into a monetary value and apply the converted monetary value to acharity account.
 27. A method for testing a market segmentation,comprising: segmenting users into one or more user segmentation cells,the user segmentation cells being associated with one or more marketsegments; generating one or more messages for the one or more usersegmentation cells associated with the users, the one or more messagesincluding an offer mix; sending the one or more messages to a subset ofthe users associated with the one or more user segmentation cells;analyzing one or more responses by the users receiving the one or moremessages to identify a type of message eliciting a high response rate;refining the messages based on the identified type of message; andsending the refined messages to all of the users of the one or moresegmentation cells.
 28. The method of claim 27, wherein generatingincludes generating one or more first messages for a first subset of theusers, and one or more second messages for a second subset of the users,wherein the first and second messages are different.
 29. The method ofclaim 28, wherein the generating, sending and analyzing are repeated fora predetermine number of times.
 30. A method for preference building,comprising: presenting one or more questions and one or more offersavailable to a user on a user interface, wherein the one or morequestions and one or more offers are dynamically created for the userbased on initial user data; receiving answers to the questions throughthe user interface; processing the answers to generate preference dataof the user; dynamically changing offers presented on the user interfacein near real-time based on the preference data.
 31. The method of claim30, wherein the initial user data is supplied by a network to which theuser belongs.
 32. The method of claim 31, wherein the network is asponsor of a loyalty/rewards program.
 33. A method for preferencebuilding, comprising: presenting a calendar interface to a user, thecalendar interface including indicia indicative of one or more pastoffers from one or more merchants; and presenting a selection interfaceto the user, the selection interface including an input field todesignate past offers that the user wishes to be reminded of in futureofferings.
 34. The system of claim 10 further comprising: (e) an offerranking module to rank the merchant's offer to other offers in the oneor more offer data stores based on popularity.
 35. The system of claim10 further comprising: (e) a heat map module to display on the one ormore client devices consumer activity on the communication network. 36.The system of claim 19, wherein the purchase transaction data isincluded in a partially qualified transaction (PQT) issued by the cardtransaction processing module.
 37. The system claim 36, wherein thepurchase transaction data includes stock keeping unit (SKU) level data.38. The system of claim 20, wherein the qualified transaction data isincluded in a fully qualified transaction (FQT) issued by the rewardsmodule
 39. The system of claim 21, wherein the purchase transaction datais included in a partially qualified transaction (PQT) issued by thecard transaction processing module.
 40. The system claim 39, wherein thepurchase transaction data includes stock keeping unit (SKU) level data.41. The system of claim 21, wherein the qualified transaction data isincluded in a fully qualified transaction (FQT) issued by the rewardsmodule